The KITTI Vision Benchmark Suite

The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

• Download left color images of object data set (12 GB)
• Download right color images, if you want to use stereo information (12 GB)
• Download the 3 temporally preceding frames (left color) (36 GB)
• Download the 3 temporally preceding frames (right color) (36 GB)
• Download Velodyne point clouds, if you want to use laser information (29 GB)
• Download camera calibration matrices of object data set (16 MB)
• Download training labels of object data set (5 MB)
• Download object development kit (1 MB) (including 3D object detection and bird's eye view evaluation code)
• Download pre-trained LSVM baseline models (5 MB) used in Joint 3D Estimation of Objects and Scene Layout (NIPS 2011). These models are referred to as LSVM-MDPM-sv (supervised version) and LSVM-MDPM-us (unsupervised version) in the tables below.
• Download reference detections (L-SVM) for training and test set (800 MB)
• Qianli Liao (NYU) has put together code to convert from KITTI to PASCAL VOC file format (documentation included, requires Emacs).
• Karl Rosaen (U.Mich) has released code to convert between KITTI, KITTI tracking, Pascal VOC, Udacity, CrowdAI and AUTTI formats.

We evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. Difficulties are defined as follows:

• Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
• Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
• Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %

All methods are ranked based on the moderately difficult results. Note that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	PC-CNN-V2			95.20 %	96.06 %	89.37 %	0.5 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
2	F-PointNet		code	95.17 %	95.85 %	85.42 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
3	ATL			95.16 %	97.92 %	90.15 %	0.04 s	1 core @ 2.5 Ghz (Python)
4	THU CV-AI			95.04 %	95.29 %	87.73 %	0.38 s	GPU @ 2.5 Ghz (Python)
5	MVRA + I-FRCNN+			94.98 %	95.87 %	82.52 %	0.18 s	GPU @ 2.5 Ghz (Python)
6	GNN3D			94.87 %	96.03 %	90.03 %	1 s	GPU @ 2.5 Ghz (Python)
7	BM-NET			94.49 %	95.09 %	85.06 %	0.5 s	GPU @ 2.5 Ghz (Python + C/C++)
8	TuSimple		code	94.47 %	95.12 %	86.45 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
9	UberATG-MMF			94.25 %	97.41 %	89.87 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
10	VCTNet			93.97 %	94.48 %	84.23 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
11	DGIST-CellBox			93.90 %	95.86 %	88.26 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
12	Patches - EMP			93.75 %	97.91 %	90.56 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
13	EM-FPS			93.70 %	94.73 %	86.38 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
14	MDC			93.68 %	96.72 %	83.76 %	0.17 s	GPU @ 2.5 Ghz (Python)
15	THICV-YDM			93.60 %	96.26 %	81.08 %	0.06 s	GPU @ 2.5 Ghz (Python)
16	HRI-SH			93.57 %	96.23 %	86.33 %	3.6 s	GPU @ >3.5 Ghz (Python + C/C++)
17	Deep MANTA			93.50 %	98.89 %	83.21 %	0.7 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
18	FichaDL			93.46 %	96.00 %	84.39 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
19	RRC		code	93.40 %	95.68 %	87.37 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
20	Alibaba-AILabsX			93.35 %	96.38 %	85.92 %	0.2 s	GPU @ >3.5 Ghz (Python)
21	CFENet			93.26 %	93.91 %	86.99 %	4 s	GPU @ 2.5 Ghz (Python + C/C++)
22	STD			93.22 %	96.14 %	90.53 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
23	Fast Point R-CNN v1			93.18 %	96.13 %	87.68 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
24	sensekitti		code	93.17 %	94.79 %	84.38 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
25	ELE			93.14 %	98.44 %	90.32 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
26	SJTU-HW			93.11 %	96.30 %	82.21 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
27	RGB3D			93.07 %	96.54 %	88.04 %	0.39 s	GPU @ 2.5 Ghz (Python)
28	DH-ARI			93.01 %	94.55 %	87.84 %	0.2 s	1 core @ >3.5 Ghz (Python + C/C++)
29	PointRCNN-deprecated			92.96 %	96.72 %	85.81 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
30	Fast Point R-CNN			92.93 %	96.02 %	87.41 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
31	SARPNET			92.84 %	95.76 %	87.64 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
32	DH-ARI			92.83 %	92.75 %	82.92 %	4s	GPU @ 2.5 Ghz (C/C++)
33	SegVoxelNet			92.73 %	96.00 %	87.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
34	Patches			92.72 %	96.34 %	87.63 %	0.15 s	GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
35	MVX-Net			92.66 %	96.06 %	85.33 %	0.06 s	GPU @ 3.0 Ghz (Python + C/C++)
36	NU-optim			92.63 %	95.67 %	87.37 %	0.04 s	GPU @ >3.5 Ghz (Python)
37	ART-Det			92.59 %	97.82 %	81.89 %	0.067s	GPU @ 2.5 Ghz (Python + C/C++)
38	SPA			92.56 %	95.96 %	87.60 %	0.1 s	1 core @ 2.5 Ghz (Python)
39	CONV-BOX			92.53 %	95.76 %	87.60 %	0.2 s	Tesla V100
40	IPOD			92.44 %	95.54 %	87.55 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
41	CP			92.44 %	96.14 %	87.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
42	YOLOv3.5			92.42 %	95.22 %	82.32 %	0.05 s	GPU @ 2.5 Ghz (Python)
43	F-ConvNet			92.19 %	95.85 %	80.09 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
44	PI-RCNN			92.15 %	96.01 %	87.18 %	0.1 s	1 core @ 2.5 Ghz (Python)
45	ECV-NET			92.07 %	94.49 %	84.25 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
46	SDP+RPN			92.03 %	95.16 %	79.16 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
47	AB3DMOT		code	92.00 %	95.88 %	86.98 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
48	MMLab-PointRCNN		code	91.90 %	95.92 %	87.11 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
49	MMLab-PartA^2			91.86 %	95.03 %	89.06 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.
50	MBR-SSD			91.83 %	93.46 %	84.97 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
51	epBRM			91.77 %	94.59 %	88.45 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
52	ITVD		code	91.73 %	95.85 %	79.31 %	0.3 s	GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
53	HRI-FusionRCNN			91.70 %	94.61 %	84.10 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
54	SINet+		code	91.67 %	94.17 %	78.60 %	0.3 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
55	Cascade MS-CNN		code	91.60 %	94.26 %	78.84 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019. Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
56	MLF			91.59 %	94.34 %	79.14 %	0.05 s	GPU @ 2.0 Ghz (Python)
57	Det-RGBD			91.49 %	94.30 %	79.41 %	0.58 s	GPU @ 2.5 Ghz (Python + C/C++)
58	HRI-VoxelFPN			91.44 %	96.65 %	86.18 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Wang, J. An and J. Cao: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds. arXiv preprint arXiv:1907.05286v2 2019.
59	TBA			91.43 %	93.99 %	88.51 %	0.07 s	1 core @ 2.5 Ghz (Python)
60	FNV2			91.39 %	96.20 %	81.33 %	0.18 s	GPU @ 2.5 Ghz (Python)
61	PFPN			91.25 %	94.33 %	81.41 %	0.02 s	4 cores @ >3.5 Ghz (Python)
62	Alibaba-AILabsX			91.23 %	96.33 %	83.75 %	0.05 s	1 core @ >3.5 Ghz (C/C++)
63	CentrNet-v1			91.21 %	94.22 %	88.36 %	0.03 s	GPU @ 2.5 Ghz (Python)
64	PointPillars		code	91.19 %	94.00 %	88.17 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
65	LTN			91.18 %	94.68 %	81.51 %	0.4 s	GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
66	DDB			91.12 %	93.71 %	87.34 %	0.05 s	GPU @ 2.5 Ghz (Python)
67	AILabs3D			91.11 %	96.38 %	85.77 %	0.6 s	GPU @ >3.5 Ghz (Python)
68	Aston-EAS			91.02 %	93.91 %	77.93 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
69	ARPNET			90.99 %	94.00 %	83.49 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
70	MMV			90.91 %	94.16 %	83.36 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
71	A-VoxelNet			90.86 %	93.84 %	83.27 %	0.029 s	GPU @ 2.5 Ghz (Python)
72	VAT-Net			90.83 %	96.07 %	80.56 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
73	MV3D			90.83 %	96.47 %	78.63 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
74	MVSLN			90.81 %	96.12 %	83.39 %	0.1s s	1 core @ 2.5 Ghz (C/C++)
75	MPNet			90.80 %	94.68 %	87.30 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
76	3D IoU Loss			90.79 %	95.92 %	85.65 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
77	SINet_VGG		code	90.79 %	93.59 %	77.53 %	0.2 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
78	Tencent_ADlab_Lidar			90.74 %	93.80 %	86.75 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
79	SRF			90.69 %	95.88 %	85.52 %	0.05 s	GPU @ 2.5 Ghz (Python + C/C++)
80	TANet			90.67 %	93.67 %	85.31 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
81	SFB-SECOND			90.67 %	96.17 %	85.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
82	SECOND-V1.5		code	90.65 %	95.96 %	85.35 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)
83	PTS		code	90.64 %	95.74 %	85.41 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
84	FOFNet			90.52 %	94.00 %	85.20 %	0.04 s	GPU @ 2.5 Ghz (Python)
85	MP			90.50 %	93.86 %	85.17 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
86	Sogo_MM			90.46 %	94.31 %	80.62 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
87	RCN-resnet101			90.35 %	92.36 %	80.58 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
88	AtrousDet			90.35 %	95.94 %	77.94 %	0.05 s	TITAN X
89	InNet			90.30 %	95.42 %	82.05 %	0.16 s	GPU @ 3.5 Ghz (Python + C/C++)
90	SCNet			90.30 %	95.59 %	85.09 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
91	SECOND		code	90.21 %	93.79 %	82.94 %	38 ms	1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
92	Deep3DBox			90.19 %	94.71 %	76.82 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
93	FQNet			90.17 %	94.72 %	76.78 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
94	RAR-Net			90.16 %	94.72 %	76.78 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
95	SAANet			90.14 %	95.93 %	82.95 %	0.10 s	1 core @ 2.5 Ghz (Python)
96	SAG-Net			90.08 %	94.77 %	80.27 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
97	DeepStereoOP			90.06 %	95.15 %	79.91 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
98	SubCNN			89.98 %	94.26 %	79.78 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
99	MLOD		code	89.97 %	94.88 %	84.98 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
100	GPP		code	89.96 %	94.02 %	81.13 %	0.23 s	GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. arXiv preprint arXiv:1811.06666 2018.
101	ZRNet(ResNet-50)			89.92 %	95.24 %	79.69 %	0.04 s	GPU @ 2.5 Ghz (Python)
102	AVOD		code	89.88 %	95.17 %	82.83 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
103	SINet_PVA		code	89.86 %	92.72 %	76.47 %	0.11 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
104	DFD			89.72 %	93.37 %	82.41 %	0.05 s	GPU @ 2.0 Ghz (Python + C/C++)
105	ZRNet			89.72 %	93.97 %	79.47 %	0.04 s	GPU @ 2.5 Ghz (Python)
106	PP_v1.0		code	89.71 %	93.42 %	86.12 %	0.02s	1 core @ 2.5 Ghz (C/C++)
107	SeoulRobotics-HFD			89.68 %	93.30 %	84.52 %	0.035 s	GPU (C++)
108	3DOP		code	89.55 %	92.96 %	79.38 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
109	PAD			89.49 %	93.43 %	85.85 %	0.15 s	1 core @ 2.5 Ghz (Python)
110	RuiRUC			89.48 %	92.52 %	86.00 %	0.12 s	1 core @ 2.5 Ghz (Python)
111	Mono3D		code	89.37 %	94.52 %	79.15 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
112	4D-MSCNN+CRL			89.37 %	92.40 %	77.00 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
113	MonoDIS			89.15 %	94.61 %	78.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
114	cas+res+soft			89.14 %	94.54 %	78.37 %	0.2 s	4 cores @ 2.5 Ghz (Python)
115	merge12-12			88.96 %	94.58 %	78.22 %	0.2 s	4 cores @ 2.5 Ghz (Python)
116	AVOD-FPN		code	88.92 %	94.70 %	84.13 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
117	CLA			88.86 %	94.16 %	76.53 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
118	AM3D			88.71 %	92.55 %	77.78 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
119	MS-CNN		code	88.68 %	93.87 %	76.11 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
120	DA			88.63 %	94.59 %	76.00 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
121	3DNN			88.56 %	94.52 %	81.51 %	0.09 s	GPU @ 2.5 Ghz (Python)
122	ODES		code	88.53 %	90.28 %	77.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
123	MonoPSR		code	88.50 %	93.63 %	73.36 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
124	Shift R-CNN (mono)		code	88.48 %	94.07 %	78.34 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
125	CFR			88.48 %	94.12 %	80.89 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
126	MM-MRFC			88.46 %	95.54 %	78.14 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
127	TridentNet			88.37 %	90.33 %	80.57 %	0.2 s	GPU @ 2.5 Ghz (Python)
128	3DBN			88.29 %	93.74 %	80.74 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
129	NLK			87.89 %	91.65 %	83.32 %	0.02 s	1 core @ 2.5 Ghz (Python)
130	Multi-3D			87.87 %	93.70 %	76.07 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
131	ELLIOT			87.83 %	93.18 %	84.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
132	cas_retina			87.64 %	93.87 %	75.30 %	0.2 s	4 cores @ 2.5 Ghz (Python)
133	ZongMu-Mono			87.46 %	93.15 %	77.58 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
134	cascadercnn			87.36 %	89.37 %	73.42 %	0.36 s	4 cores @ 2.5 Ghz (Python)
135	SCANet			87.28 %	92.91 %	81.99 %	0.17 s	>8 cores @ 2.5 Ghz (Python)
136	SECA			87.16 %	94.95 %	80.01 %	0.09 s	GPU @ 2.5 Ghz (Python)
137	SCANet			86.94 %	92.69 %	79.95 %	0.09s	GPU @ 2.5 Ghz (Python)
138	anm			86.52 %	94.88 %	76.46 %	3 s	1 core @ 2.5 Ghz (C/C++)
139	ReSqueeze			86.12 %	90.35 %	76.53 %	0.03 s	GPU @ >3.5 Ghz (Python)
140	IoU_DCRCNN			86.07 %	90.04 %	78.14 %	0.66 s	GPU @ 2.5 Ghz (Python)
141	Stereo R-CNN		code	85.98 %	93.98 %	71.25 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
142	StereoFENet			85.70 %	91.48 %	77.62 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. 2019.
143	ResNet-RRC w/RGBD			85.58 %	91.32 %	74.80 %	0.057 s	GPU @ 1.5 Ghz (Python + C/C++)
144	X_MD			85.52 %	93.31 %	78.25 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
145	cas_retina_1_13			85.48 %	91.54 %	74.60 %	0.03 s	4 cores @ 2.5 Ghz (Python)
146	NEUAV			85.42 %	89.67 %	77.28 %	0.06 s	GPU @ 2.5 Ghz (Python)
147	ResNet-RRC			85.33 %	91.45 %	74.27 %	0.06 s	GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and . others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
148	Cmerge			85.32 %	93.40 %	70.57 %	0.2 s	4 cores @ 2.5 Ghz (Python)
149	PL V2 (SDN+GDC)			85.15 %	94.95 %	77.78 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
150	M3D-RPN		code	85.08 %	89.04 %	69.26 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
151	FNV1_RPN			85.07 %	94.59 %	79.91 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
152	FNV1_Fusion			85.02 %	92.64 %	79.77 %	0.11 s	GPU @ 2.5 Ghz (Python)
153	SDP+CRC (ft)			85.00 %	92.06 %	71.71 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
154	SS3D			84.92 %	92.72 %	70.35 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
155	LPN			84.77 %	89.19 %	74.08 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
156	MonoFENet			84.63 %	91.68 %	76.71 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. 2019.
157	SECA			84.60 %	92.51 %	79.53 %	1 s	GPU @ 2.5 Ghz (Python)
158	VSE			84.60 %	92.51 %	79.53 %	0.15 s	GPU @ 2.5 Ghz (Python)
159	MV3D (LIDAR)			84.39 %	93.08 %	79.27 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
160	Complexer-YOLO			84.16 %	91.92 %	79.62 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
161	YOLOv3+d			84.09 %	86.66 %	75.08 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
162	softretina			83.30 %	93.55 %	70.59 %	0.16 s	4 cores @ 2.5 Ghz (Python)
163	FNV1			83.20 %	91.34 %	75.93 %	0.11 s	GPU @ 2.5 Ghz (Python)
164	Faster R-CNN		code	83.16 %	88.97 %	72.62 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
165	ZKNet			82.96 %	92.17 %	72.43 %	0.01 s	GPU @ 2.0 Ghz (Python)
166	Pseudo-LiDAR V2		code	82.90 %	94.46 %	75.45 %	0.4 s	GPU @ 2.5 Ghz (Python)
167	Retinanet100			82.73 %	93.97 %	68.37 %	0.2 s	4 cores @ 2.5 Ghz (Python)
168	BS3D			82.72 %	95.35 %	70.01 %	22 ms	Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
169	cascade_gw			82.35 %	85.98 %	71.60 %	0.2 s	4 cores @ 2.5 Ghz (Python)
170	FRCNN+Or		code	82.00 %	92.91 %	68.79 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
171	detectron		code	81.51 %	91.43 %	69.50 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
172	Resnet101Faster rcnn			81.44 %	91.08 %	71.52 %	1 s	1 core @ 2.5 Ghz (Python)
173	A3DODWTDA (image)		code	81.25 %	78.96 %	70.56 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
174	RefineNet			81.01 %	91.91 %	65.67 %	0.20 s	GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016. R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
175	MTDP			80.97 %	89.03 %	66.91 %	0.15 s	GPU @ 2.0 Ghz (Python)
176	RFCN_RFB			80.89 %	88.07 %	69.66 %	0.2 s	4 cores @ 2.5 Ghz (Python)
177	Manhnet			80.85 %	89.06 %	64.29 %	26 ms	1 core @ 2.5 Ghz (C/C++)
178	SeRC			80.82 %	89.95 %	69.04 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
179	centernet			80.78 %	90.29 %	70.53 %	0.01 s	GPU @ 2.5 Ghz (Python)
180	RADNet-Fusion			80.04 %	76.72 %	76.78 %	0.1 s	1 core @ 2.5 Ghz (Python)
181	NM		code	79.98 %	90.71 %	68.98 %	0.01 s	GPU @ 2.5 Ghz (Python)
182	RADNet-LIDAR			79.59 %	75.20 %	76.03 %	0.1 s	1 core @ 2.5 Ghz (Python)
183	SceneNet			79.26 %	90.70 %	67.98 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
184	A3DODWTDA		code	79.15 %	82.98 %	68.30 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
185	CLF3D			79.05 %	87.57 %	67.58 %	0.13 s	GPU @ 2.5 Ghz (Python)
186	spLBP			78.66 %	81.66 %	61.69 %	1.5 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
187	3D-SSMFCNN		code	78.19 %	77.92 %	69.19 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
188	MonoGRNet		code	77.94 %	88.65 %	63.31 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
189	yolov3_warp			77.61 %	92.24 %	65.70 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
190	Reinspect		code	77.48 %	90.27 %	66.73 %	2s	1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
191	multi-task CNN			77.18 %	86.12 %	68.09 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
192	Regionlets			76.99 %	88.75 %	60.49 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
193	3DVP		code	76.98 %	84.95 %	65.78 %	40 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
194	FailNet-Fusion			76.90 %	74.55 %	71.94 %	0.1 s	1 core @ 2.5 Ghz (Python)
195	RTL3D			76.74 %	79.68 %	72.56 %	0.02 s	GPU @ 2.5 Ghz (Python)
196	avodC			76.58 %	87.30 %	71.65 %	0.1 s	GPU @ 2.5 Ghz (Python)
197	SubCat		code	76.36 %	84.10 %	60.56 %	0.7 s	6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
198	GS3D			76.35 %	86.23 %	62.67 %	2 s	1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
199	FailNet-LIDAR			76.26 %	74.16 %	71.24 %	0.1 s	1 core @ 2.5 Ghz (Python)
200	AOG		code	76.24 %	86.08 %	61.51 %	3 s	4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016. B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
201	bin			76.16 %	78.73 %	63.39 %	15ms s	GPU @ >3.5 Ghz (Python)
202	Pose-RCNN			75.83 %	89.59 %	64.06 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
203	VoxelNet(Unofficial)			75.22 %	81.37 %	68.74 %	0.5 s	GPU @ 2.0 Ghz (Python)
204	RFCN			75.14 %	83.04 %	61.55 %	0.2 s	4 cores @ 2.5 Ghz (Python)
205	myfaster-rcnn-v1.5			74.93 %	89.85 %	62.56 %	0.1 s	1 core @ 2.5 Ghz (Python)
206	3D FCN			74.65 %	86.74 %	67.85 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
207	OC Stereo			74.60 %	87.39 %	62.56 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
208	yolo800			74.31 %	78.93 %	63.83 %	0.13 s	4 cores @ 2.5 Ghz (Python)
209	ResNet-RRC (Noised)			74.30 %	79.15 %	64.80 %	.057 s	GPU @ 1.5 Ghz (Python + C/C++)
210	3DVSSD			74.11 %	86.99 %	63.57 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
211	Multi-task DG			74.07 %	91.06 %	64.48 %	0.06 s	GPU @ 2.5 Ghz (Python)
212	FD2			73.93 %	88.65 %	64.62 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
213	BdCost+DA+BB+MS			73.72 %	85.18 %	57.79 %	TBD s	4 cores @ 2.5 Ghz (Matlab + C/C++)
214	BdCost+DA+MS			73.62 %	85.03 %	58.94 %	TBD s	4 cores @ 2.5 Ghz (Matlab/C++)
215	Int-YOLO		code	73.23 %	75.81 %	63.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
216	MF3D			71.85 %	91.50 %	57.46 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
217	RFBnet			71.66 %	87.25 %	63.00 %	0.2 s	4 cores @ 2.5 Ghz (Python)
218	AOG-View			71.26 %	85.01 %	55.73 %	3 s	1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
219	GPVL			71.06 %	81.67 %	54.96 %	10 s	1 core @ 2.5 Ghz (C/C++)
220	BdCost+DA+BB			70.86 %	85.52 %	56.19 %	TBD s	4 cores @ 2.5 Ghz (Matlab + C/C++)
221	MV-RGBD-RF			70.70 %	77.89 %	57.41 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
222	Vote3Deep			70.30 %	78.95 %	63.12 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
223	ROI-10D			70.16 %	76.56 %	61.15 %	0.2 s	GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
224	fasterrcnn			69.45 %	74.76 %	60.20 %	0.2 s	4 cores @ 2.5 Ghz (Python)
225	myfaster-rcnn			68.38 %	90.54 %	55.97 %	0.01 s	1 core @ 2.5 Ghz (Python)
226	Decoupled-3D			67.92 %	87.78 %	54.53 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
227	Pseudo-LiDAR		code	67.79 %	85.40 %	58.50 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. CVPR 2019.
228	SA_3D			67.50 %	88.90 %	53.04 %	0.3 s	GPU @ 2.5 Ghz (Python)
229	OC-DPM			67.06 %	79.07 %	52.61 %	10 s	8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
230	mymask-rcnn			66.82 %	88.60 %	52.18 %	0.3 s	1 core @ 2.5 Ghz (Python)
231	Fast-SSD			66.79 %	85.19 %	57.89 %	0.06 s	GTX650Ti
232	DPM-VOC+VP			66.72 %	82.15 %	49.01 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
233	BdCost48LDCF		code	66.63 %	81.38 %	52.20 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
234	E-VoxelNet			65.33 %	68.00 %	57.84 %	0.1 s	GPU @ 2.5 Ghz (Python)
235	BdCost48-25C			64.63 %	81.42 %	52.22 %	4 s	1 core @ 2.5 Ghz (C/C++)
236	MDPM-un-BB			64.06 %	79.74 %	49.07 %	60 s	4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
237				64.06 %	81.75 %	54.83 %
238	PDV-Subcat			63.24 %	78.27 %	47.67 %	7 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
239	MODet			62.54 %	66.06 %	60.04 %	0.05 s	GTX1080Ti
240	yl_net			61.78 %	66.00 %	60.36 %	0.03 s	GPU @ 2.5 Ghz (Python)
241	Lidar_ROI+Yolo(UJS)			61.71 %	73.32 %	53.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
242	GNN			61.48 %	79.09 %	51.06 %	0.2 s	1 core @ 2.5 Ghz (Python)
243	SubCat48LDCF		code	61.16 %	78.86 %	44.69 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
244	DPM-C8B1			60.21 %	75.24 %	44.73 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
245	tiny_rfdet		code	59.94 %	65.51 %	57.20 %	0.01 s	GPU @ 2.5 Ghz (Python)
246	RADNet-Mono			59.85 %	67.47 %	54.14 %	0.1 s	1 core @ 2.5 Ghz (Python)
247	monoref3d			58.97 %	78.11 %	47.72 %	0.1 s	1 core @ 2.5 Ghz (Python)
248	ref3D			58.97 %	78.11 %	47.72 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
249	100Frcnn			58.92 %	82.09 %	49.04 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
250	SAMME48LDCF		code	58.38 %	77.47 %	44.43 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
251	LSVM-MDPM-sv			58.36 %	71.11 %	43.22 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
252	ref3D			57.16 %	77.96 %	45.99 %	0.1 s	1 core @ 2.5 Ghz (Python)
253	BirdNet			57.02 %	78.91 %	55.08 %	0.11 s	Titan Xp GPU
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
254	ACF-SC			56.60 %	69.90 %	43.61 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
255	LSVM-MDPM-us		code	55.95 %	68.94 %	41.45 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
256	mylsi-faster-rcnn			55.81 %	80.45 %	47.38 %	0.3 s	1 core @ 2.5 Ghz (Python)
257	ACF			54.09 %	63.05 %	41.81 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
258	Mono3D_PLiDAR		code	53.36 %	80.85 %	44.80 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
259	VeloFCN			51.82 %	70.53 %	45.70 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
260	FailNet-Mono			47.95 %	59.59 %	41.33 %	0.1 s	1 core @ 2.5 Ghz (Python)
261	DLMB			46.50 %	60.92 %	41.59 %	0.03 s	8 cores @ 3.5 Ghz (C/C++)
262	softyolo			45.97 %	66.08 %	38.02 %	0.16 s	4 cores @ 2.5 Ghz (Python)
263	Vote3D			45.94 %	54.38 %	40.48 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
264	TopNet-HighRes			45.85 %	58.04 %	41.11 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
265	RT3DStereo			45.81 %	56.53 %	37.63 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
266	Multimodal Detection		code	45.46 %	63.91 %	37.25 %	0.06 s	GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
267	rpn			40.80 %	67.42 %	32.16 %	0.01 s	1 core @ 2.5 Ghz (Python)
268	RT3D			39.69 %	50.33 %	40.04 %	0.09 s	GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
269	Licar			35.19 %	42.34 %	33.97 %	0.09 s	GPU @ 2.0 Ghz (Python)
270	KD53-20			34.76 %	51.76 %	29.39 %	0.19 s	4 cores @ 2.5 Ghz (Python)
271	DT3D			34.07 %	50.81 %	31.46 %	0,21s	GPU @ 2.5 Ghz (Python)
272	SAIC-SA-3D			31.16 %	41.51 %	29.83 %	0.05 s	GPU @ 2.5 Ghz (Python)
273	FCN-Depth		code	25.05 %	52.32 %	18.07 %	1 s	GPU @ 1.5 Ghz (Matlab + C/C++)
274	CSoR		code	21.66 %	31.52 %	17.99 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
275	mBoW			21.59 %	35.22 %	16.89 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
276	R-CNN_VGG			21.36 %	29.38 %	16.61 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
277	DepthCN		code	21.18 %	37.45 %	16.08 %	2.3 s	GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
278	DLnet			15.90 %	21.22 %	13.78 %	0.3 s	4 cores @ 2.5 Ghz (C/C++)
279	YOLOv2		code	14.31 %	26.74 %	10.94 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
280	TopNet-UncEst			6.24 %	7.24 %	5.42 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
281	TopNet-Retina			5.00 %	6.82 %	4.52 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
282	FCPP			0.07 %	0.01 %	0.07 %	0.02 s	1 core @ 2.0 Ghz (Python + C/C++)
283	TopNet-DecayRate			0.01 %	0.00 %	0.01 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
284	LaserNet			0.00 %	0.00 %	0.00 %	12 ms	GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
285	SN-net			0.00 %	0.00 %	0.00 %	0.8 s	GPU @ 2.5 Ghz (Python + C/C++)
286	JSyolo			0.00 %	0.00 %	0.00 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	FichaDL			82.50 %	90.75 %	75.66 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
2	DGIST-CellBox			81.29 %	90.04 %	76.92 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
3	Alibaba-CityBrain			81.19 %	90.99 %	74.68 %	1.5 s	GPU @ 2.5 Ghz (Python + C/C++)
4	ExtAtt			81.05 %	90.60 %	76.08 %	1.2 s	GPU @ 2.5 Ghz (Python + C/C++)
5	F-PointNet		code	80.13 %	89.83 %	75.05 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
6	DH-ARI			78.79 %	88.87 %	71.76 %	3.6 s	GPU @ 2.5 Ghz (Python + C/C++)
7	EM-FPS			78.50 %	85.58 %	73.68 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
8	TuSimple		code	78.40 %	88.87 %	73.66 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
9	Argus_detection_v1			77.01 %	84.86 %	72.15 %	0.25 s	GPU @ 1.5 Ghz (C/C++)
10	VCTNet			76.68 %	86.56 %	71.43 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
11	RRC		code	76.61 %	85.98 %	71.47 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
12	ECP Faster R-CNN			76.25 %	85.96 %	70.55 %	0.25 s	GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
13	Aston-EAS			76.07 %	86.71 %	70.02 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
14	MHN			75.99 %	87.21 %	69.50 %	0.39 s	GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
15	FFNet		code	75.81 %	87.17 %	69.86 %	1.07 s	GPU @ 1.5 Ghz (Python)
16	SJTU-HW			75.81 %	87.17 %	69.86 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
17	THICV-YDM			75.65 %	89.04 %	68.72 %	0.06 s	GPU @ 2.5 Ghz (Python)
18	Multi-3D			75.32 %	86.00 %	69.55 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
19	CLA			75.16 %	86.02 %	69.24 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
20	MS-CNN		code	74.89 %	85.71 %	68.99 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
21	BOE_IOT_AIBD			74.74 %	86.06 %	69.44 %	0.8 s	GPU @ 2.5 Ghz (Python)
22	SAITv1			73.47 %	86.68 %	68.40 %	0.15 s	GPU @ 2.5 Ghz (C/C++)
23	F-ConvNet			72.91 %	83.63 %	67.18 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
24	Sogo_MM			72.82 %	84.99 %	67.42 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
25	ECV-NET			72.73 %	86.52 %	66.15 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
26	GN			72.29 %	82.93 %	65.56 %	1 s	GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
27	SubCNN			72.27 %	84.88 %	66.82 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
28	VMVS			71.82 %	82.80 %	66.85 %	0.25 s	GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
29	IVA		code	71.37 %	84.61 %	64.90 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
30	MDC			71.13 %	86.16 %	66.15 %	0.17 s	GPU @ 2.5 Ghz (Python)
31	MM-MRFC			70.76 %	83.79 %	64.81 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
32	SDP+RPN			70.42 %	82.07 %	65.09 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
33	TridentNet			70.07 %	83.42 %	65.28 %	0.2 s	GPU @ 2.5 Ghz (Python)
34	3DOP		code	69.57 %	83.17 %	63.48 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
35	DA			69.22 %	82.75 %	63.96 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
36	HBA-RCNN			68.68 %	79.07 %	63.65 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
37	MonoPSR		code	68.56 %	85.60 %	63.34 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
38	DeepStereoOP			68.46 %	83.00 %	63.35 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
39	sensekitti		code	68.41 %	82.72 %	62.72 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
40	ODES		code	68.10 %	79.98 %	61.69 %	0.02 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
41	Mono3D		code	67.29 %	80.30 %	62.23 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
42	YOLOv3.5			66.54 %	83.87 %	61.45 %	0.05 s	GPU @ 2.5 Ghz (Python)
43	Faster R-CNN		code	66.24 %	79.97 %	61.09 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
44	AtrousDet			64.97 %	80.79 %	58.36 %	0.05 s	TITAN X
45	SDP+CRC (ft)			64.36 %	79.22 %	59.16 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
46	Pose-RCNN			63.54 %	80.07 %	57.02 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
47	PCN			63.41 %	80.08 %	58.55 %	0.6 s
48	CFM			62.84 %	74.76 %	56.06 %	<2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
49	merge12-12			62.84 %	80.27 %	56.08 %	0.2 s	4 cores @ 2.5 Ghz (Python)
50	cas+res+soft			62.71 %	80.11 %	55.99 %	0.2 s	4 cores @ 2.5 Ghz (Python)
51	cas_retina			62.37 %	79.82 %	57.15 %	0.2 s	4 cores @ 2.5 Ghz (Python)
52	cas_retina_1_13			61.87 %	79.09 %	56.70 %	0.03 s	4 cores @ 2.5 Ghz (Python)
53	ReSqueeze			61.33 %	73.69 %	56.65 %	0.03 s	GPU @ >3.5 Ghz (Python)
54	RPN+BF		code	61.22 %	77.06 %	55.22 %	0.6 s	GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
55	IPOD			61.14 %	74.79 %	56.54 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
56	Regionlets			60.83 %	73.79 %	54.72 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
57	bin			60.73 %	71.43 %	55.78 %	15ms s	GPU @ >3.5 Ghz (Python)
58	anm			60.35 %	76.02 %	55.46 %	3 s	1 core @ 2.5 Ghz (C/C++)
59	cascadercnn			59.50 %	78.79 %	54.44 %	0.36 s	4 cores @ 2.5 Ghz (Python)
60	ALV303			59.49 %	71.70 %	55.66 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
61	A-VoxelNet			59.07 %	69.90 %	56.49 %	0.029 s	GPU @ 2.5 Ghz (Python)
62	TANet			59.07 %	69.90 %	56.44 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
63	mymask-rcnn			58.89 %	72.55 %	52.58 %	0.3 s	1 core @ 2.5 Ghz (Python)
64	DDB			58.53 %	69.03 %	55.90 %	0.05 s	GPU @ 2.5 Ghz (Python)
65	DeepParts			58.15 %	71.47 %	51.92 %	~1 s	GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
66	CompACT-Deep			58.14 %	70.93 %	52.29 %	1 s	1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
67	AVOD-FPN		code	57.87 %	67.95 %	55.23 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
68	LPN			57.69 %	71.87 %	53.21 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
69	LDAM			56.68 %	64.73 %	54.21 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
70	FRCNN+Or		code	56.68 %	71.64 %	51.53 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
71	yolo800			56.67 %	71.26 %	50.91 %	0.13 s	4 cores @ 2.5 Ghz (Python)
72	ZKNet			56.58 %	71.15 %	51.87 %	0.01 s	GPU @ 2.0 Ghz (Python)
73	CentrNet-v1			56.57 %	66.27 %	54.19 %	0.03 s	GPU @ 2.5 Ghz (Python)
74	FilteredICF			56.53 %	69.79 %	50.32 %	~ 2 s	>8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
75	Tencent_ADlab_Lidar			56.49 %	66.91 %	54.06 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
76	ARPNET			56.42 %	69.08 %	52.69 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
77	FD2			56.35 %	71.37 %	51.08 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
78	MV-RGBD-RF			56.18 %	72.99 %	49.72 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
79	RFCN			55.96 %	72.32 %	49.66 %	0.2 s	4 cores @ 2.5 Ghz (Python)
80	MLOD		code	55.62 %	68.42 %	51.45 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
81	PointPillars		code	55.10 %	65.29 %	52.39 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
82	CONV-BOX			55.06 %	64.43 %	51.06 %	0.2 s	Tesla V100
83	STD			55.04 %	68.33 %	50.85 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
84	RFCN_RFB			54.98 %	70.61 %	48.75 %	0.2 s	4 cores @ 2.5 Ghz (Python)
85	SA_3D			54.89 %	69.83 %	48.26 %	0.3 s	GPU @ 2.5 Ghz (Python)
86	Vote3Deep			54.80 %	67.99 %	51.17 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
87	CHTTL MMF			54.28 %	72.79 %	49.31 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
88	epBRM			54.13 %	62.90 %	51.95 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
89	NM		code	54.05 %	69.81 %	49.32 %	0.01 s	GPU @ 2.5 Ghz (Python)
90	PDV2			53.54 %	65.59 %	47.65 %	3.7 s	1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
91	fasterrcnn			53.42 %	69.29 %	48.76 %	0.2 s	4 cores @ 2.5 Ghz (Python)
92	PP_v1.0		code	53.32 %	63.06 %	50.86 %	0.02s	1 core @ 2.5 Ghz (C/C++)
93	TAFT			53.15 %	67.62 %	47.08 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
94	pAUCEnsT			52.88 %	65.84 %	46.97 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
95	Multi-task DG			52.87 %	71.27 %	48.10 %	0.06 s	GPU @ 2.5 Ghz (Python)
96	SECOND		code	52.81 %	66.53 %	48.47 %	38 ms	1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
97	NEUAV			52.53 %	68.50 %	47.99 %	0.06 s	GPU @ 2.5 Ghz (Python)
98	detectron		code	52.36 %	71.51 %	47.51 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
99	MLF			52.32 %	67.93 %	47.77 %	0.05 s	GPU @ 2.0 Ghz (Python)
100	YOLOv3+d			51.94 %	68.35 %	45.60 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
101	MTDP			51.81 %	68.12 %	46.95 %	0.15 s	GPU @ 2.0 Ghz (Python)
102	GNN3D			51.45 %	60.74 %	47.95 %	1 s	GPU @ 2.5 Ghz (Python)
103	Shift R-CNN (mono)		code	51.30 %	70.86 %	46.37 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
104	CLF3D			51.24 %	67.07 %	44.98 %	0.13 s	GPU @ 2.5 Ghz (Python)
105	SCANet			51.23 %	65.06 %	47.06 %	0.17 s	>8 cores @ 2.5 Ghz (Python)
106	centernet			51.09 %	69.27 %	45.40 %	0.01 s	GPU @ 2.5 Ghz (Python)
107	myfaster-rcnn-v1.5			50.95 %	67.68 %	46.29 %	0.1 s	1 core @ 2.5 Ghz (Python)
108	FOFNet			50.08 %	62.64 %	46.27 %	0.04 s	GPU @ 2.5 Ghz (Python)
109	SCNet			49.61 %	60.95 %	46.91 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
110	MMLab-PointRCNN		code	49.41 %	58.93 %	46.44 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
111	Resnet101Faster rcnn			49.12 %	64.72 %	44.60 %	1 s	1 core @ 2.5 Ghz (Python)
112	cascade_gw			48.99 %	67.35 %	44.25 %	0.2 s	4 cores @ 2.5 Ghz (Python)
113	Int-YOLO		code	48.76 %	64.09 %	44.31 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
114	MP			48.73 %	60.26 %	45.05 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
115	ACFD		code	48.63 %	61.62 %	44.15 %	0.2 s	4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
116	R-CNN			48.57 %	62.88 %	43.05 %	4 s	GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
117	mylsi-faster-rcnn			47.99 %	65.03 %	43.43 %	0.3 s	1 core @ 2.5 Ghz (Python)
118	SeRC			47.62 %	63.23 %	43.03 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
119	ELLIOT			46.84 %	58.72 %	43.91 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
120	Cmerge			46.51 %	63.68 %	41.60 %	0.2 s	4 cores @ 2.5 Ghz (Python)
121	CFR			45.85 %	60.88 %	43.37 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
122	SS3D			45.79 %	61.58 %	41.14 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
123	ACF			45.67 %	59.81 %	40.88 %	1 s	1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
124	Fusion-DPM		code	44.99 %	58.93 %	40.19 %	~ 30 s	1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
125	ACF-MR			44.79 %	58.29 %	39.94 %	0.6 s	1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
126	HA-SSVM			43.87 %	58.76 %	38.81 %	21 s	1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
127	AB3DMOT		code	43.86 %	54.55 %	40.99 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
128	DPM-VOC+VP			43.26 %	59.21 %	38.12 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
129	ACF-SC			42.97 %	53.30 %	38.12 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
130	SquaresICF		code	42.61 %	57.08 %	37.85 %	1 s	GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
131	GNN			42.28 %	58.09 %	37.81 %	0.2 s	1 core @ 2.5 Ghz (Python)
132	CSW3D			41.50 %	53.76 %	37.25 %	0.03 s	4 cores @ 2.5 Ghz (C/C++)
133	M3D-RPN		code	41.46 %	56.64 %	37.31 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
134	myfaster-rcnn			41.22 %	56.87 %	36.99 %	0.01 s	1 core @ 2.5 Ghz (Python)
135	yolov3_warp			40.64 %	55.04 %	36.33 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
136	SubCat			40.50 %	53.75 %	35.66 %	1.2 s	6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
137	SAANet			40.43 %	51.16 %	38.38 %	0.10 s	1 core @ 2.5 Ghz (Python)
138	Retinanet100			40.03 %	54.30 %	35.33 %	0.2 s	4 cores @ 2.5 Ghz (Python)
139	AVOD		code	39.43 %	50.90 %	35.75 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
140	softyolo			39.30 %	54.49 %	36.66 %	0.16 s	4 cores @ 2.5 Ghz (Python)
141	ACF			39.12 %	48.42 %	35.03 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
142	pedestrian_cnn			37.90 %	52.07 %	33.58 %	1 s	1 core @ 2.5 Ghz (C/C++)
143	LSVM-MDPM-sv			37.26 %	50.74 %	33.13 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
144	multi-task CNN			37.00 %	49.38 %	33.46 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
145	X_MD			36.61 %	48.20 %	33.09 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
146	Complexer-YOLO			36.45 %	42.16 %	32.91 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
147	KD53-20			36.03 %	45.78 %	32.79 %	0.19 s	4 cores @ 2.5 Ghz (Python)
148	LSVM-MDPM-us		code	35.92 %	48.73 %	31.70 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
149	Lidar_ROI+Yolo(UJS)			35.58 %	47.74 %	31.48 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
150				34.81 %	44.38 %	32.10 %
151	anonymous			34.58 %	45.41 %	32.53 %	0.75 s	GPU @ 3.5 Ghz (C/C++)
152	Vote3D			33.04 %	42.66 %	30.59 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
153	rpn			31.92 %	43.34 %	27.84 %	0.01 s	1 core @ 2.5 Ghz (Python)
154	OC Stereo			30.79 %	43.50 %	28.40 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
155	mBoW			30.26 %	41.52 %	26.34 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
156	BirdNet			29.58 %	36.62 %	28.25 %	0.11 s	Titan Xp GPU
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
157	RT3DStereo			29.30 %	41.12 %	25.25 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
158	DPM-C8B1			25.34 %	36.40 %	22.00 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
159	100Frcnn			21.92 %	34.07 %	19.48 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
160	R-CNN_VGG			19.97 %	26.62 %	17.96 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
161	TopNet-Retina			16.45 %	22.37 %	15.43 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
162	TopNet-HighRes			15.28 %	21.22 %	13.89 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
163	DT3D			14.89 %	20.46 %	13.61 %	0,21s	GPU @ 2.5 Ghz (Python)
164	YOLOv2		code	11.46 %	15.37 %	9.67 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
165	TopNet-UncEst			8.58 %	13.00 %	7.38 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
166	BIP-HETERO			7.05 %	8.51 %	6.30 %	~2 s	1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
167	softretina			0.26 %	0.19 %	0.26 %	0.16 s	4 cores @ 2.5 Ghz (Python)
168	JSyolo			0.12 %	0.19 %	0.12 %	0.16 s	4 cores @ 2.5 Ghz (Python)
169	TopNet-DecayRate			0.01 %	0.01 %	0.01 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
170	SN-net			0.00 %	0.00 %	0.00 %	0.8 s	GPU @ 2.5 Ghz (Python + C/C++)

Cyclist

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	EM-FPS			81.22 %	85.45 %	72.28 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
2	FichaDL			80.38 %	88.41 %	69.72 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
3	F-ConvNet			78.05 %	86.75 %	68.12 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
4	VCTNet			77.28 %	84.58 %	68.22 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
5	SAITv1			77.21 %	86.39 %	66.75 %	0.15 s	GPU @ 2.5 Ghz (C/C++)
6	RRC		code	76.81 %	86.81 %	66.59 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
7	Multi-3D			75.77 %	84.71 %	65.95 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
8	CLA			75.48 %	84.80 %	65.48 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
9	MS-CNN		code	75.30 %	84.88 %	65.27 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
10	TuSimple		code	75.22 %	83.68 %	65.22 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
11	ExtAtt			75.08 %	86.09 %	65.30 %	1.2 s	GPU @ 2.5 Ghz (Python + C/C++)
12	Deep3DBox			74.78 %	84.36 %	64.05 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
13	SDP+RPN			73.85 %	82.59 %	64.87 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
14	sensekitti		code	73.48 %	82.90 %	64.03 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
15	MMLab-PointRCNN		code	73.42 %	86.21 %	66.45 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
16	F-PointNet		code	73.16 %	86.86 %	65.21 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
17	FOFNet			72.96 %	87.12 %	66.37 %	0.04 s	GPU @ 2.5 Ghz (Python)
18	ECV-NET			72.93 %	84.19 %	63.24 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
19	BOE_IOT_AIBD			72.34 %	84.64 %	63.52 %	0.8 s	GPU @ 2.5 Ghz (Python)
20	MonoPSR		code	72.08 %	82.06 %	62.43 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
21	ARPNET			71.95 %	84.96 %	65.21 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
22	SubCNN			71.72 %	79.36 %	62.74 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
23	STD			71.63 %	83.99 %	64.92 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
24	Sogo_MM			71.57 %	79.35 %	62.22 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
25	MDC			70.22 %	84.28 %	60.95 %	0.17 s	GPU @ 2.5 Ghz (Python)
26	AB3DMOT		code	70.18 %	82.86 %	63.55 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
27	ODES		code	69.82 %	80.14 %	60.37 %	0.02 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
28	TridentNet			69.63 %	81.97 %	59.52 %	0.2 s	GPU @ 2.5 Ghz (Python)
29	MP			69.52 %	85.05 %	63.17 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
30	LDAM			69.31 %	80.20 %	63.85 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
31	PointPillars		code	68.98 %	83.97 %	62.17 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
32	DGIST-CellBox			68.92 %	83.72 %	61.32 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
33	Vote3Deep			68.82 %	78.41 %	62.50 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
34	3DOP		code	68.71 %	80.52 %	61.07 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
35	Pose-RCNN			68.40 %	81.53 %	59.43 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
36	TANet			68.20 %	82.24 %	62.13 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
37	GNN3D			67.86 %	80.27 %	62.44 %	1 s	GPU @ 2.5 Ghz (Python)
38	Tencent_ADlab_Lidar			67.82 %	82.74 %	61.06 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
39	IVA		code	67.57 %	78.48 %	58.83 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
40	A-VoxelNet			67.37 %	81.32 %	60.27 %	0.029 s	GPU @ 2.5 Ghz (Python)
41	DeepStereoOP			67.22 %	79.35 %	58.60 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
42	SAANet			66.58 %	83.07 %	59.88 %	0.10 s	1 core @ 2.5 Ghz (Python)
43	epBRM			66.51 %	79.65 %	60.31 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
44	IPOD			65.25 %	83.72 %	57.81 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
45	Mono3D		code	65.15 %	77.19 %	57.88 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
46	CONV-BOX			64.53 %	72.81 %	58.13 %	0.2 s	Tesla V100
47	DA			63.60 %	80.72 %	56.15 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
48	MLF			63.34 %	83.91 %	53.78 %	0.05 s	GPU @ 2.0 Ghz (Python)
49	CentrNet-v1			62.99 %	78.90 %	56.46 %	0.03 s	GPU @ 2.5 Ghz (Python)
50	Faster R-CNN		code	62.86 %	72.40 %	54.97 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
51	AtrousDet			62.50 %	79.02 %	53.87 %	0.05 s	TITAN X
52	SCNet			62.50 %	78.48 %	56.34 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
53	SCANet			62.31 %	76.50 %	56.06 %	0.17 s	>8 cores @ 2.5 Ghz (Python)
54	DDB			61.41 %	78.04 %	55.37 %	0.05 s	GPU @ 2.5 Ghz (Python)
55	SECOND		code	60.96 %	81.73 %	54.13 %	38 ms	1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
56	AVOD-FPN		code	60.79 %	70.38 %	55.37 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
57	SDP+CRC (ft)			60.72 %	75.63 %	53.00 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
58	CFR			60.04 %	76.63 %	53.40 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
59	Complexer-YOLO			59.78 %	66.94 %	55.63 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
60	ELLIOT			59.78 %	78.40 %	54.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
61	PP_v1.0		code	59.48 %	77.50 %	52.86 %	0.02s	1 core @ 2.5 Ghz (C/C++)
62	merge12-12			59.48 %	77.66 %	51.41 %	0.2 s	4 cores @ 2.5 Ghz (Python)
63	cas+res+soft			59.43 %	77.85 %	51.34 %	0.2 s	4 cores @ 2.5 Ghz (Python)
64	YOLOv3.5			58.57 %	79.16 %	51.74 %	0.05 s	GPU @ 2.5 Ghz (Python)
65	Regionlets			58.52 %	71.12 %	50.83 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
66	cascadercnn			58.08 %	77.24 %	51.13 %	0.36 s	4 cores @ 2.5 Ghz (Python)
67	bin			57.62 %	64.36 %	50.70 %	15ms s	GPU @ >3.5 Ghz (Python)
68	cas_retina			57.14 %	73.97 %	50.32 %	0.2 s	4 cores @ 2.5 Ghz (Python)
69	FRCNN+Or		code	57.01 %	70.99 %	50.14 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
70	cas_retina_1_13			56.39 %	72.80 %	49.71 %	0.03 s	4 cores @ 2.5 Ghz (Python)
71	MLOD		code	56.04 %	75.35 %	49.11 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
72	Multi-task DG			55.30 %	75.48 %	48.22 %	0.06 s	GPU @ 2.5 Ghz (Python)
73	ReSqueeze			54.50 %	69.64 %	48.24 %	0.03 s	GPU @ >3.5 Ghz (Python)
74	AVOD		code	52.60 %	66.45 %	46.39 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
75	ZKNet			49.48 %	66.29 %	42.81 %	0.01 s	GPU @ 2.0 Ghz (Python)
76	anm			49.05 %	66.96 %	43.44 %	3 s	1 core @ 2.5 Ghz (C/C++)
77	NEUAV			48.65 %	69.50 %	42.64 %	0.06 s	GPU @ 2.5 Ghz (Python)
78	LPN			48.57 %	65.77 %	42.66 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
79	mylsi-faster-rcnn			47.90 %	69.04 %	41.72 %	0.3 s	1 core @ 2.5 Ghz (Python)
80	fasterrcnn			47.87 %	64.39 %	42.03 %	0.2 s	4 cores @ 2.5 Ghz (Python)
81	BirdNet			47.64 %	64.97 %	44.66 %	0.11 s	Titan Xp GPU
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
82	yolo800			47.31 %	63.22 %	42.28 %	0.13 s	4 cores @ 2.5 Ghz (Python)
83	detectron		code	47.26 %	65.49 %	40.82 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
84	X_MD			46.97 %	62.50 %	40.84 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
85	RFCN			46.70 %	62.09 %	40.71 %	0.2 s	4 cores @ 2.5 Ghz (Python)
86	CLF3D			46.46 %	64.85 %	40.10 %	0.13 s	GPU @ 2.5 Ghz (Python)
87	NM		code	45.82 %	60.69 %	40.83 %	0.01 s	GPU @ 2.5 Ghz (Python)
88	RFCN_RFB			45.28 %	60.06 %	39.66 %	0.2 s	4 cores @ 2.5 Ghz (Python)
89	Cmerge			44.87 %	64.38 %	37.80 %	0.2 s	4 cores @ 2.5 Ghz (Python)
90	YOLOv3+d			43.00 %	60.88 %	38.15 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
91	Shift R-CNN (mono)		code	42.96 %	63.24 %	38.22 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
92	myfaster-rcnn-v1.5			42.89 %	59.60 %	38.07 %	0.1 s	1 core @ 2.5 Ghz (Python)
93	cascade_gw			42.84 %	63.58 %	36.94 %	0.2 s	4 cores @ 2.5 Ghz (Python)
94	FD2			42.67 %	62.54 %	38.41 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
95	centernet			42.45 %	58.95 %	37.56 %	0.01 s	GPU @ 2.5 Ghz (Python)
96	SeRC			41.93 %	56.95 %	36.47 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
97	M3D-RPN		code	41.54 %	61.54 %	35.23 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
98	MV-RGBD-RF			40.94 %	51.10 %	34.83 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
99	MTDP			40.46 %	53.83 %	35.74 %	0.15 s	GPU @ 2.0 Ghz (Python)
100	Int-YOLO		code	39.83 %	53.34 %	34.16 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
101	GNN			39.80 %	58.30 %	34.56 %	0.2 s	1 core @ 2.5 Ghz (Python)
102	myfaster-rcnn			35.81 %	54.28 %	31.82 %	0.01 s	1 core @ 2.5 Ghz (Python)
103	SS3D			35.48 %	52.97 %	31.07 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
104	pAUCEnsT			34.90 %	50.51 %	30.35 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
105	Retinanet100			32.30 %	46.60 %	28.29 %	0.2 s	4 cores @ 2.5 Ghz (Python)
106	TopNet-Retina			31.98 %	47.51 %	29.84 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
107	yolov3_warp			29.48 %	44.46 %	25.84 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
108	OC Stereo			28.76 %	43.18 %	24.80 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
109	Vote3D			27.99 %	39.81 %	25.19 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
110	softyolo			27.90 %	41.90 %	24.74 %	0.16 s	4 cores @ 2.5 Ghz (Python)
111	LSVM-MDPM-us		code	27.81 %	37.66 %	24.83 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
112	DPM-VOC+VP			27.73 %	41.58 %	24.61 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
113	100Frcnn			27.69 %	43.23 %	23.91 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
114	LSVM-MDPM-sv			26.05 %	35.70 %	23.56 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
115	DPM-C8B1			25.57 %	41.47 %	21.93 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
116	R-CNN_VGG			25.14 %	34.28 %	22.17 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
117	Lidar_ROI+Yolo(UJS)			24.42 %	36.43 %	21.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
118	rpn			23.44 %	36.85 %	21.25 %	0.01 s	1 core @ 2.5 Ghz (Python)
119	DT3D			18.25 %	30.12 %	17.20 %	0,21s	GPU @ 2.5 Ghz (Python)
120	mBoW			17.63 %	26.66 %	16.02 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
121	SA_3D			14.38 %	19.40 %	12.50 %	0.3 s	GPU @ 2.5 Ghz (Python)
122	TopNet-HighRes			13.98 %	22.86 %	14.52 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
123	mymask-rcnn			13.58 %	18.03 %	12.42 %	0.3 s	1 core @ 2.5 Ghz (Python)
124	RT3DStereo			12.96 %	19.58 %	11.47 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
125	KD53-20			12.81 %	20.05 %	11.99 %	0.19 s	4 cores @ 2.5 Ghz (Python)
126	TopNet-UncEst			12.00 %	18.14 %	11.85 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
127	softretina			0.25 %	0.16 %	0.18 %	0.16 s	4 cores @ 2.5 Ghz (Python)
128	YOLOv2		code	0.06 %	0.15 %	0.07 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
129	TopNet-DecayRate			0.04 %	0.00 %	0.04 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
130	JSyolo			0.03 %	0.02 %	0.04 %	0.16 s	4 cores @ 2.5 Ghz (Python)