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	THU CV-AI			91.97 %	91.96 %	84.57 %	0.38 s	GPU @ 2.5 Ghz (Python)
2	DH-ARI			91.48 %	90.87 %	82.25 %	4s	GPU @ 2.5 Ghz (C/C++)
3	HRI-SH			90.71 %	91.34 %	84.28 %	3.6 s	GPU @ >3.5 Ghz (Python + C/C++)
4	BM-NET			90.50 %	90.81 %	83.92 %	0.5 s	GPU @ 2.5 Ghz (Python + C/C++)
5	MVRA + I-FRCNN+			90.36 %	90.78 %	80.48 %	0.18 s	GPU @ 2.5 Ghz (Python)
6	TuSimple		code	90.33 %	90.77 %	82.86 %	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.
7	RRC		code	90.23 %	90.61 %	87.44 %	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.
8	CFENet			90.22 %	90.33 %	84.85 %	4 s	GPU @ 2.5 Ghz (Python + C/C++)
9	MMF			90.17 %	91.82 %	88.54 %	0.08 s	GPU @ 2.5 Ghz (Python)
10	EM-FPS			90.15 %	90.61 %	84.01 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
11	icst-SSD			90.08 %	90.30 %	84.70 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
12	SJTU-HW			90.08 %	90.81 %	79.98 %	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.
13	MDC			90.03 %	90.72 %	80.87 %	0.2 s	volta v100
14	Deep MANTA			90.03 %	97.25 %	80.62 %	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.
15	sensekitti		code	90.00 %	90.76 %	81.83 %	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.
16	F-PointNet		code	90.00 %	90.78 %	80.80 %	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	ECV-NET			89.93 %	90.61 %	81.81 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
18	ART-Det			89.89 %	95.18 %	80.03 %	0.067s	GPU @ 2.5 Ghz (Python + C/C++)
19	FNV2			89.88 %	90.51 %	80.66 %	0.18 s	GPU @ 2.5 Ghz (Python)
20	MBR-SSD			89.82 %	90.32 %	82.28 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
21	PointRCNN			89.75 %	90.77 %	80.98 %	0.2 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. arXiv preprint arXiv:1812.04244 2018.
22	SINet+		code	89.73 %	90.51 %	77.82 %	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 2018.
23	Alibaba-AILabsX			89.69 %	90.59 %	80.83 %	0.2 s	GPU @ >3.5 Ghz (Python)
24	AILabs3D			89.68 %	90.57 %	80.67 %	0.6 s	GPU @ >3.5 Ghz (Python)
25	Paul-Fr-RCNN			89.59 %	90.76 %	77.23 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
26	D3D			89.59 %	90.51 %	80.57 %	0.4 s	1 core @ 3.5 Ghz (Python)
27	SINet_VGG		code	89.56 %	90.60 %	78.19 %	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 2018.
28	Fast Point R-CNN			89.51 %	90.58 %	87.89 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
29	DH-ARI			89.47 %	90.31 %	84.78 %	0.2 s	1 core @ >3.5 Ghz (Python + C/C++)
30	SDP+RPN			89.42 %	89.90 %	78.54 %	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.
31	VAT-Net			89.41 %	90.69 %	79.97 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
32	IPOD			89.30 %	90.20 %	87.37 %	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. arXiv preprint arXiv:1812.05276 2018.
33	MVX-Net			89.25 %	90.43 %	80.47 %	0.06 s	GPU @ 3.0 Ghz (Python + C/C++)
34	ITVD		code	89.23 %	90.57 %	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.
35	PointPillars			89.22 %	90.33 %	87.04 %	16 ms	1080ti GPU and Intel i7 CPU
36	CONV-BOX			89.20 %	90.35 %	87.88 %	0.2 s	Tesla V100
37	VCTNet			89.20 %	89.60 %	80.04 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
38	Sogo_MM			89.17 %	90.80 %	79.58 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
39	MV3D			89.17 %	90.53 %	80.16 %	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.
40	SINet_PVA		code	89.08 %	90.44 %	75.85 %	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 2018.
41	LTT			89.00 %	90.16 %	81.94 %	0.4 s	1 core @ 3.5 Ghz (Python)
42	CLA			88.99 %	90.51 %	75.50 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
43	InNet			88.95 %	90.26 %	79.46 %	0.16 s	GPU @ 3.5 Ghz (Python + C/C++)
44	SubCNN			88.86 %	90.75 %	79.24 %	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.
45	Deep3DBox			88.86 %	90.47 %	77.60 %	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.
46	desNet			88.85 %	90.51 %	79.36 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
47	MonoPSR			88.84 %	90.18 %	71.44 %	0.2 s	GPU @ 3.5 Ghz (Python)
48	MS-CNN		code	88.83 %	90.46 %	74.76 %	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.
49	RoarNet		code	88.80 %	90.69 %	79.46 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
K. Shin, Y. Kwon and M. Tomizuka: RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement. arXiv preprint arXiv:1811.03818 2018.
50	vfnet			88.77 %	89.63 %	79.55 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
51	RCN-resnet101			88.75 %	89.08 %	79.97 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
52	DeepStereoOP			88.75 %	90.34 %	79.39 %	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.
53	SAG-Net			88.61 %	89.25 %	79.72 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
54	ARPNET			88.44 %	90.26 %	79.86 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
55	SECOND		code	88.40 %	90.40 %	80.21 %	0.05 s	4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
56	3DOP		code	88.34 %	90.09 %	78.79 %	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.
57	DA			88.23 %	90.45 %	74.21 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
58	MM-MRFC			88.20 %	90.93 %	78.02 %	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.
59	AVOD		code	88.08 %	89.73 %	80.14 %	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.
60	SeoulRobotics-HFD			87.86 %	90.02 %	79.95 %	0.05 s	GPU @ 2.0 Ghz (Python + C/C++)
61	Mono3D		code	87.86 %	90.27 %	78.09 %	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.
62	merge12-12			87.81 %	89.88 %	77.42 %	0.2 s	4 cores @ 2.5 Ghz (Python)
63	DFD			87.78 %	90.02 %	79.78 %	0.05 s	GPU @ 2.0 Ghz (Python + C/C++)
64	AVOD-FPN		code	87.44 %	89.99 %	80.05 %	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.
65	SECA			87.42 %	89.57 %	79.43 %	0.09 s	GPU @ 2.5 Ghz (Python)
66	SCANet			87.31 %	89.34 %	79.30 %	0.09s	GPU @ 2.5 Ghz (Python)
67	CNN-ds		code	87.15 %	86.86 %	70.77 %	0.05 s	1 core @ 2.5 Ghz (Python)
68	ODES		code	87.10 %	86.82 %	78.32 %	0.02 s	GPU @ 2.5 Ghz (Python)
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69	ELLIOT			86.98 %	90.20 %	81.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
70	cascadercnn			85.86 %	84.21 %	69.57 %	0.36 s	4 cores @ 2.5 Ghz (Python)
71	ReSqueeze			85.74 %	87.12 %	77.02 %	0.03 s	GPU @ >3.5 Ghz (Python)
72	anm			85.33 %	90.11 %	76.55 %	3 s	1 core @ 2.5 Ghz (C/C++)
73	YOLOv3+d			84.13 %	84.30 %	76.34 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
74	LPN			81.67 %	87.70 %	72.69 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
75	A3DODWTDA (image)		code	81.54 %	76.21 %	66.85 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
76	SDP+CRC (ft)			81.33 %	90.39 %	70.33 %	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.
77	Stereo R-CNN			81.10 %	90.41 %	71.79 %	0.4 s	GPU @ 2.5 Ghz (Python)
78	ResNet-RRC w/RGBD			81.09 %	89.91 %	71.78 %	0.057 s	GPU @ 1.5 Ghz (Python + C/C++)
79	ResNet-RRC (Adv. HW)			81.00 %	89.89 %	71.56 %	0.06 s	GPU @ 1.5 Ghz (Python + C/C++)
80	X_MD			80.65 %	89.81 %	79.66 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
81	FNV1_Fusion			80.41 %	89.37 %	79.03 %	0.11 s	GPU @ 2.5 Ghz (Python)
82	FNV1_RPN			80.41 %	89.44 %	79.14 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
83	Cmerge			80.25 %	89.83 %	70.76 %	0.2 s	4 cores @ 2.5 Ghz (Python)
84	SS3D			80.17 %	89.31 %	70.51 %	88 ms	Tesla V100 (Python)
85	VSE			80.05 %	89.26 %	78.80 %	0.15 s	GPU @ 2.5 Ghz (Python)
86	MV3D (LIDAR)			79.76 %	89.80 %	78.61 %	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.
87	ZKNet			79.62 %	89.89 %	70.03 %	0.01 s	GPU @ 2.0 Ghz (Python)
88	RFCN_RFB			79.45 %	83.85 %	67.51 %	0.2 s	4 cores @ 2.5 Ghz (Python)
89	Complexer-YOLO			79.31 %	88.11 %	79.11 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
90	FNV1			79.28 %	88.45 %	77.14 %	0.11 s	GPU @ 2.5 Ghz (Python)
91	RefineNet			79.21 %	90.16 %	65.71 %	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.
92	retinanetkitti			79.18 %	85.90 %	70.04 %	1.5 s	1 core @ 2.5 Ghz (Python)
93	softretina			79.15 %	89.36 %	69.24 %	0.16 s	4 cores @ 2.5 Ghz (Python)
94	Faster R-CNN		code	79.11 %	87.90 %	70.19 %	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.
95	Kiwoo			79.06 %	89.23 %	70.50 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
96	detectron		code	78.96 %	88.14 %	69.74 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
97	FRCNN+Or		code	78.95 %	89.87 %	68.97 %	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.
98	Resnet101Faster rcnn			78.93 %	87.97 %	69.80 %	1 s	1 core @ 2.5 Ghz (Python)
99	Retinanet100			78.85 %	89.83 %	68.73 %	0.2 s	4 cores @ 2.5 Ghz (Python)
100	SeRC			78.33 %	88.28 %	69.36 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
101	T2Method			78.26 %	88.55 %	69.76 %	0.05 s	GPU @ 2.5 Ghz (Python + C/C++)
102	avodC			77.54 %	86.86 %	70.00 %	0.1 s	GPU @ 2.5 Ghz (Python)
103	spLBP			77.39 %	80.16 %	60.59 %	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.
104	SceneNet			77.34 %	87.90 %	68.38 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
105	CLF3D			77.00 %	84.51 %	67.81 %	0.13 s	GPU @ 2.5 Ghz (Python)
106	MTDP			76.91 %	84.24 %	67.91 %	0.15 s	GPU @ 2.0 Ghz (Python)
107	NLK			76.81 %	85.26 %	74.78 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
108	Reinspect		code	76.65 %	88.36 %	66.56 %	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.
109	Regionlets			76.56 %	86.50 %	59.82 %	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.
110	AOG		code	75.97 %	85.58 %	60.96 %	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.
111	GS3D			75.84 %	83.92 %	60.24 %	0.58 s	GPU @ 2.5 Ghz (C/C++)
112	3D FCN			75.83 %	85.54 %	68.30 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
113	3D-SSMFCNN		code	75.78 %	75.51 %	67.75 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
114	3DVP		code	75.77 %	81.46 %	65.38 %	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.
115	Pose-RCNN			75.74 %	88.89 %	61.86 %	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.
116	SubCat		code	75.46 %	81.45 %	59.71 %	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.
117	DimStr-LKY			75.22 %	81.21 %	67.28 %	0.1 s	GPU @ 2.5 Ghz (Matlab + C/C++)
118	multi-task CNN			75.21 %	83.45 %	66.89 %	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.
119	A3DODWTDA		code	74.71 %	78.21 %	66.70 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
120	FD2			74.68 %	87.14 %	65.70 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
121	BdCost+			74.07 %	83.02 %	59.06 %	TBD s	4 cores @ 2.5 Ghz (Matlab/C++)
122	RFCN			73.56 %	80.70 %	61.94 %	0.2 s	4 cores @ 2.5 Ghz (Python)
123	tiny-det			73.46 %	81.88 %	63.70 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
124	3DVSSD			73.39 %	84.39 %	65.64 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
125	bin			73.31 %	76.05 %	63.76 %	15ms s	GPU @ >3.5 Ghz (Python)
126	yolo800			73.00 %	76.45 %	64.68 %	0.13 s	4 cores @ 2.5 Ghz (Python)
127	ResNet-RRC (Noised)			71.81 %	78.97 %	63.57 %	.057 s	GPU @ 1.5 Ghz (Python + C/C++)
128	MV-RGBD-RF			69.92 %	76.49 %	57.47 %	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.
129	AOG-View			69.89 %	84.29 %	57.25 %	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.
130	ROI-10D			69.64 %	75.33 %	61.18 %	0.2 s	GPU @ 3.5 Ghz (Python)
131	fasterrcnn			68.76 %	73.64 %	59.72 %	0.2 s	4 cores @ 2.5 Ghz (Python)
132	MF3D			68.72 %	88.46 %	58.70 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
133	Vote3Deep			68.39 %	76.95 %	63.22 %	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.
134	PL			67.96 %	85.08 %	59.55 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
135	GPVL			67.89 %	77.76 %	58.23 %	10 s	1 core @ 2.5 Ghz (C/C++)
136	RFBnet			67.86 %	82.31 %	59.95 %	0.2 s	4 cores @ 2.5 Ghz (Python)
137	Fast-SSD			67.17 %	83.89 %	59.09 %	0.06 s	GTX650Ti
138	BdCost48LDCF		code	67.08 %	77.93 %	51.15 %	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.
139	VoxelNet(Unofficial)			66.77 %	65.64 %	60.74 %	0.5 s	GPU @ 2.0 Ghz (Python)
140	OC-DPM			66.45 %	76.16 %	53.70 %	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.
141	DPM-VOC+VP			66.25 %	80.45 %	49.86 %	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.
142	BdCost48-25C			65.95 %	78.21 %	51.23 %	4 s	1 core @ 2.5 Ghz (C/C++)
143	PL			65.93 %	78.51 %	57.77 %	0.4 s	GPU @ 2.5 Ghz (Python)
144	MDPM-un-BB			64.20 %	77.32 %	50.18 %	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.
145	PDV-Subcat			63.15 %	77.33 %	49.75 %	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.
146	Lidar_ROI+Yolo(UJS)			62.71 %	70.58 %	55.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
147	GNN			62.59 %	76.03 %	50.18 %	0.2 s	1 core @ 2.5 Ghz (Python)
148	DPM-C8B1			60.99 %	74.95 %	47.16 %	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.
149	SubCat48LDCF		code	60.53 %	78.16 %	43.66 %	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.
150	Fast-SSD			60.24 %	83.39 %	51.96 %	0.06 s	GTX650Ti
151	SA-3D			59.14 %	69.84 %	49.62 %	0.3 s	1 core @ 2.5 Ghz (Python)
152	SAMME48LDCF		code	58.50 %	76.22 %	47.50 %	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.
153	BirdNet			57.47 %	78.18 %	56.66 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
154	100Frcnn			57.47 %	81.09 %	48.37 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
155	LSVM-MDPM-sv			57.44 %	71.70 %	46.58 %	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.
156	LSVM-MDPM-us		code	56.10 %	70.52 %	42.87 %	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.
157	ACF-SC			55.76 %	69.76 %	46.27 %	<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.
158	VeloFCN			53.45 %	70.68 %	46.90 %	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 .
159	ACF			52.81 %	62.82 %	43.89 %	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). .
160	TopNet-HighRes			48.87 %	59.77 %	43.15 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
161	Vote3D			48.05 %	56.66 %	42.64 %	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.
162	Multimodal Detection		code	46.77 %	64.04 %	39.38 %	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.
163	softyolo			45.77 %	62.82 %	39.77 %	0.16 s	4 cores @ 2.5 Ghz (Python)
164	rpn			43.99 %	65.47 %	36.33 %	0.01 s	1 core @ 2.5 Ghz (Python)
165	VoxelNet basic			43.44 %	45.07 %	39.59 %	0.07 s	GPU (Python)
166	RT3D			39.71 %	49.96 %	41.47 %	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.
167	KD53-20			37.82 %	52.30 %	32.71 %	0.19 s	4 cores @ 2.5 Ghz (Python)
168	DT3D			35.98 %	49.23 %	31.78 %	0,21s	GPU @ 2.5 Ghz (Python)
169	KD45			34.36 %	42.94 %	30.99 %	0.16 s	4 cores @ 2.5 Ghz (Python)
170	Licar			33.89 %	41.60 %	35.17 %	0.09 s	GPU @ 2.0 Ghz (Python)
171	Kyolo3			33.01 %	47.18 %	27.57 %	0.16 s	4 cores @ 2.5 Ghz (Python)
172	CSoR		code	26.13 %	35.24 %	22.69 %	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.
173	R-CNN_VGG			26.04 %	32.23 %	20.93 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
174	FCN-Depth		code	25.66 %	50.55 %	24.95 %	1 s	GPU @ 1.5 Ghz (Matlab + C/C++)
175	mBoW			23.76 %	37.63 %	18.44 %	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.
176	DepthCN		code	23.21 %	37.59 %	18.00 %	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.
177	DLnet			20.30 %	23.46 %	17.96 %	0.3 s	4 cores @ 2.5 Ghz (C/C++)
178	YOLOv2		code	19.31 %	28.37 %	15.94 %	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.
179	TopNet-DecayRate			0.04 %	0.04 %	0.04 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
180	SN-net			0.00 %	0.00 %	0.00 %	0.8 s	GPU @ 2.5 Ghz (Python + C/C++)
181	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	Alibaba-CityBrain			80.90 %	88.13 %	74.08 %	1.5 s	GPU @ 2.5 Ghz (Python + C/C++)
2	DH-ARI			78.29 %	87.43 %	69.91 %	3.6 s	GPU @ 2.5 Ghz (Python + C/C++)
3	EM-FPS			77.61 %	84.93 %	72.52 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
4	F-PointNet		code	77.25 %	87.81 %	74.46 %	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.
5	TuSimple		code	77.04 %	86.78 %	72.40 %	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.
6	Argus_detection_v1			75.51 %	83.49 %	71.24 %	0.25 s	GPU @ 1.5 Ghz (C/C++)
7	RRC		code	75.33 %	84.16 %	70.39 %	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.
8	VCTNet			75.22 %	85.49 %	71.55 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
9	ECP Faster R-CNN			74.27 %	84.12 %	70.06 %	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.
10	SJTU-HW			74.24 %	85.42 %	69.34 %	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.
11	CLA			74.03 %	84.26 %	68.45 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
12	ECV-NET			73.74 %	84.58 %	66.35 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
13	BOE_IOT_AIBD			73.73 %	84.67 %	68.71 %	0.8 s	GPU @ 2.5 Ghz (Python)
14	MS-CNN		code	73.62 %	83.70 %	68.28 %	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.
15	retinanetkitti			73.40 %	82.94 %	69.04 %	1.5 s	1 core @ 2.5 Ghz (Python)
16	SAITv1			72.61 %	84.79 %	67.94 %	0.15 s	GPU @ 2.5 Ghz (C/C++)
17	Sogo_MM			71.84 %	83.45 %	67.00 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
18	GN			71.55 %	80.73 %	64.82 %	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.
19	SubCNN			71.34 %	83.17 %	66.36 %	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.
20	IVA		code	70.63 %	83.03 %	64.68 %	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.
21	SDP+RPN			70.20 %	79.98 %	64.84 %	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.
22	MM-MRFC			69.96 %	82.37 %	64.76 %	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.
23	MDC			69.58 %	86.37 %	68.44 %	0.2 s	volta v100
24	MonoPSR			68.91 %	85.93 %	60.83 %	0.2 s	GPU @ 3.5 Ghz (Python)
25	HBA-RCNN			68.26 %	77.76 %	62.86 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
26	DA			67.89 %	79.91 %	64.83 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
27	3DOP		code	67.46 %	82.36 %	64.71 %	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.
28	DeepStereoOP			67.32 %	82.50 %	65.14 %	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.
29	sensekitti		code	67.28 %	80.12 %	62.25 %	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.
30	ODES		code	67.25 %	77.95 %	62.28 %	0.02 s	GPU @ 2.5 Ghz (Python)
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31	Mono3D		code	66.66 %	77.30 %	63.44 %	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.
32	Faster R-CNN		code	65.91 %	78.35 %	61.19 %	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.
33	SDP+CRC (ft)			64.25 %	77.81 %	59.31 %	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.
34	PCN			63.48 %	77.88 %	58.59 %	0.6 s
35	Pose-RCNN			63.38 %	77.69 %	57.42 %	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	CFM			63.26 %	74.21 %	56.44 %	<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.
37	IPOD			63.07 %	73.28 %	56.71 %	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. arXiv preprint arXiv:1812.05276 2018.
38	ALV303			61.77 %	69.13 %	54.54 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
39	RPN+BF		code	61.29 %	75.58 %	56.08 %	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.
40	ReSqueeze			61.25 %	72.78 %	57.43 %	0.03 s	GPU @ >3.5 Ghz (Python)
41	Regionlets			61.16 %	72.96 %	55.22 %	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.
42	merge12-12			60.66 %	78.15 %	58.67 %	0.2 s	4 cores @ 2.5 Ghz (Python)
43	cascadercnn			60.64 %	77.88 %	52.69 %	0.36 s	4 cores @ 2.5 Ghz (Python)
44	bin			60.54 %	70.13 %	56.55 %	15ms s	GPU @ >3.5 Ghz (Python)
45	anm			59.21 %	75.51 %	56.49 %	3 s	1 core @ 2.5 Ghz (C/C++)
46	CompACT-Deep			58.73 %	69.70 %	52.69 %	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.
47	DeepParts			58.68 %	70.46 %	52.73 %	~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.
48	AVOD-FPN		code	58.42 %	67.32 %	57.44 %	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.
49	LPN			58.18 %	70.54 %	54.18 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
50	SA-3D			57.40 %	68.11 %	50.09 %	0.3 s	1 core @ 2.5 Ghz (Python)
51	FilteredICF			57.12 %	69.05 %	51.46 %	~ 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.
52	ZKNet			57.11 %	70.20 %	52.15 %	0.01 s	GPU @ 2.0 Ghz (Python)
53	RFCN			56.91 %	71.17 %	50.06 %	0.2 s	4 cores @ 2.5 Ghz (Python)
54	FRCNN+Or		code	56.78 %	71.18 %	52.86 %	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.
55	FD2			56.68 %	71.09 %	51.65 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
56	MV-RGBD-RF			56.59 %	73.05 %	49.63 %	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.
57	CHTTL MMF			56.01 %	73.22 %	50.26 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
58	RFCN_RFB			55.86 %	69.32 %	49.18 %	0.2 s	4 cores @ 2.5 Ghz (Python)
59	SECOND		code	55.74 %	65.73 %	49.08 %	0.05 s	4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
60	PointPillars			55.68 %	64.66 %	53.93 %	16 ms	1080ti GPU and Intel i7 CPU
61	yolo800			55.49 %	71.11 %	53.92 %	0.13 s	4 cores @ 2.5 Ghz (Python)
62	Vote3Deep			55.38 %	67.94 %	52.62 %	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.
63	CONV-BOX			55.23 %	63.98 %	54.18 %	0.2 s	Tesla V100
64	TAFT			54.59 %	67.07 %	48.48 %	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.
65	pAUCEnsT			54.58 %	66.11 %	48.49 %	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.
66	fasterrcnn			53.80 %	69.00 %	51.35 %	0.2 s	4 cores @ 2.5 Ghz (Python)
67	PDV2			53.74 %	65.71 %	49.47 %	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.
68	MTDP			52.97 %	66.97 %	47.64 %	0.15 s	GPU @ 2.0 Ghz (Python)
69	detectron		code	52.42 %	69.89 %	51.70 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
70	YOLOv3+d			51.03 %	67.23 %	48.87 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
71	ACFD		code	50.91 %	61.59 %	45.51 %	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.
72	CLF3D			50.25 %	66.10 %	48.66 %	0.13 s	GPU @ 2.5 Ghz (Python)
73	R-CNN			50.20 %	62.05 %	44.85 %	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.
74	SeRC			49.81 %	65.31 %	42.40 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
75	Resnet101Faster rcnn			49.64 %	64.97 %	48.47 %	1 s	1 core @ 2.5 Ghz (Python)
76	ARPNET			48.77 %	63.16 %	47.65 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
77	ELLIOT			48.26 %	59.05 %	45.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
78	tiny-det			47.81 %	62.02 %	45.53 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
79	ACF			47.29 %	60.11 %	42.90 %	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.
80	Fusion-DPM		code	46.67 %	59.38 %	42.05 %	~ 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.
81	ACF-MR			46.23 %	58.85 %	42.10 %	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.
82	HA-SSVM			45.51 %	58.91 %	41.08 %	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.
83	DPM-VOC+VP			44.86 %	59.60 %	40.37 %	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.
84	Cmerge			44.81 %	62.62 %	44.53 %	0.2 s	4 cores @ 2.5 Ghz (Python)
85	ACF-SC			44.77 %	54.20 %	39.57 %	<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.
86	SquaresICF		code	44.42 %	57.47 %	40.08 %	1 s	GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
87	SS3D			43.51 %	60.11 %	42.62 %	88 ms	Tesla V100 (Python)
88	AVOD		code	43.49 %	51.64 %	37.79 %	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.
89	Retinanet100			42.83 %	52.43 %	35.02 %	0.2 s	4 cores @ 2.5 Ghz (Python)
90	GNN			42.56 %	58.22 %	40.53 %	0.2 s	1 core @ 2.5 Ghz (Python)
91	SubCat			42.34 %	54.06 %	37.95 %	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.
92	softyolo			40.78 %	55.95 %	39.57 %	0.16 s	4 cores @ 2.5 Ghz (Python)
93	ACF			40.62 %	49.08 %	36.66 %	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). .
94	multi-task CNN			40.34 %	51.38 %	34.98 %	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.
95	KD45			40.10 %	49.05 %	36.22 %	0.16 s	4 cores @ 2.5 Ghz (Python)
96	KD53-20			39.90 %	47.15 %	35.32 %	0.19 s	4 cores @ 2.5 Ghz (Python)
97	LSVM-MDPM-sv			39.36 %	51.75 %	35.95 %	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.
98	pedestrian_cnn			39.07 %	53.60 %	37.91 %	1 s	1 core @ 2.5 Ghz (C/C++)
99	Lidar_ROI+Yolo(UJS)			38.76 %	47.11 %	32.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
100	LSVM-MDPM-us		code	38.35 %	50.01 %	34.78 %	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.
101				37.45 %	45.89 %	35.08 %
102	X_MD			37.38 %	50.17 %	36.49 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
103	anonymous			36.65 %	49.15 %	36.18 %	0.75 s	GPU @ 3.5 Ghz (C/C++)
104	NLK			36.48 %	42.71 %	34.93 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
105	Complexer-YOLO			36.10 %	42.63 %	35.57 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
106	Vote3D			35.74 %	44.47 %	33.72 %	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.
107	rpn			32.79 %	46.95 %	31.70 %	0.01 s	1 core @ 2.5 Ghz (Python)
108	mBoW			31.37 %	44.36 %	30.62 %	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.
109	BirdNet			30.90 %	36.83 %	29.93 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
110	DPM-C8B1			29.03 %	38.96 %	25.61 %	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.
111	100Frcnn			26.73 %	35.65 %	26.46 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
112	R-CNN_VGG			23.16 %	28.95 %	22.17 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
113	Kyolo3			20.99 %	25.73 %	20.51 %	0.16 s	4 cores @ 2.5 Ghz (Python)
114	DT3D			19.19 %	27.02 %	18.98 %	0,21s	GPU @ 2.5 Ghz (Python)
115	TopNet-HighRes			17.57 %	22.98 %	17.35 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
116	Fast-SSD			16.30 %	23.14 %	16.06 %	0.06 s	GTX650Ti
117	YOLOv2		code	16.19 %	20.80 %	15.43 %	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.
118	BIP-HETERO			13.38 %	14.85 %	13.25 %	~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.
119	softretina			0.93 %	0.68 %	0.95 %	0.16 s	4 cores @ 2.5 Ghz (Python)
120	JSyolo			0.44 %	0.35 %	0.45 %	0.16 s	4 cores @ 2.5 Ghz (Python)
121	TopNet-DecayRate			0.04 %	0.02 %	0.05 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
122	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			79.24 %	84.28 %	71.22 %	0.15 s	GPU @ 1.5 Ghz (Python + C/C++)
2	RRC		code	76.49 %	84.96 %	65.46 %	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.
3	VCTNet			75.91 %	83.20 %	67.81 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
4	SAITv1			75.83 %	83.99 %	66.45 %	0.15 s	GPU @ 2.5 Ghz (C/C++)
5	CLA			74.68 %	82.42 %	65.11 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
6	MS-CNN		code	74.45 %	82.34 %	64.91 %	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.
7	TuSimple		code	74.26 %	81.38 %	64.88 %	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.
8	Deep3DBox			73.48 %	82.65 %	64.11 %	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.
9	SDP+RPN			73.08 %	81.05 %	64.88 %	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.
10	ECV-NET			72.73 %	82.62 %	62.82 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
11	sensekitti		code	72.50 %	81.76 %	64.00 %	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.
12	F-PointNet		code	72.25 %	84.90 %	65.14 %	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.
13	BOE_IOT_AIBD			71.61 %	82.63 %	63.67 %	0.8 s	GPU @ 2.5 Ghz (Python)
14	SubCNN			70.77 %	77.82 %	62.71 %	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.
15	Sogo_MM			70.72 %	77.57 %	62.23 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
16	ARPNET			69.94 %	79.83 %	63.08 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
17	ODES		code	69.80 %	78.51 %	61.32 %	0.02 s	GPU @ 2.5 Ghz (Python)
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18	MonoPSR			68.99 %	79.80 %	60.19 %	0.2 s	GPU @ 3.5 Ghz (Python)
19	MDC			68.84 %	79.81 %	60.24 %	0.2 s	volta v100
20	3DOP		code	68.81 %	80.17 %	61.36 %	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.
21	PointPillars			68.57 %	82.59 %	62.37 %	16 ms	1080ti GPU and Intel i7 CPU
22	Pose-RCNN			68.04 %	80.19 %	59.95 %	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.
23	Vote3Deep			67.96 %	76.49 %	62.88 %	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.
24	IVA		code	67.36 %	77.63 %	59.62 %	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.
25	DeepStereoOP			65.72 %	77.00 %	57.74 %	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.
26	IPOD			65.28 %	82.90 %	57.63 %	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. arXiv preprint arXiv:1812.05276 2018.
27	retinanetkitti			64.44 %	77.60 %	57.66 %	1.5 s	1 core @ 2.5 Ghz (Python)
28	Mono3D		code	63.85 %	75.22 %	58.96 %	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.
29	CONV-BOX			63.84 %	72.62 %	56.69 %	0.2 s	Tesla V100
30	DA			63.58 %	79.36 %	56.80 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
31	Faster R-CNN		code	62.81 %	71.41 %	55.44 %	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.
32	SDP+CRC (ft)			60.87 %	74.31 %	53.95 %	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.
33	merge12-12			60.83 %	75.12 %	53.69 %	0.2 s	4 cores @ 2.5 Ghz (Python)
34	ELLIOT			60.04 %	77.40 %	55.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
35	AVOD-FPN		code	59.32 %	68.65 %	55.82 %	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.
36	SECOND		code	58.94 %	81.96 %	57.20 %	0.05 s	4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
37	Regionlets			58.69 %	70.09 %	51.81 %	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.
38	cascadercnn			58.09 %	75.56 %	50.19 %	0.36 s	4 cores @ 2.5 Ghz (Python)
39	Complexer-YOLO			57.53 %	65.82 %	57.47 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
40	FRCNN+Or		code	57.37 %	70.05 %	51.00 %	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.
41	bin			57.13 %	63.05 %	50.64 %	15ms s	GPU @ >3.5 Ghz (Python)
42	AVOD		code	56.01 %	65.72 %	48.89 %	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.
43	ReSqueeze			54.93 %	68.34 %	49.19 %	0.03 s	GPU @ >3.5 Ghz (Python)
44	NLK			52.74 %	60.66 %	48.49 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
45	anm			50.54 %	67.40 %	45.22 %	3 s	1 core @ 2.5 Ghz (C/C++)
46	tiny-det			50.48 %	63.78 %	44.23 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
47	ZKNet			50.24 %	66.44 %	44.19 %	0.01 s	GPU @ 2.0 Ghz (Python)
48	LPN			50.02 %	65.33 %	44.85 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
49	yolo800			49.15 %	64.64 %	43.58 %	0.13 s	4 cores @ 2.5 Ghz (Python)
50	BirdNet			49.04 %	64.88 %	46.61 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
51	fasterrcnn			48.81 %	64.40 %	42.74 %	0.2 s	4 cores @ 2.5 Ghz (Python)
52	X_MD			48.07 %	63.46 %	40.76 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
53	detectron		code	48.06 %	64.73 %	40.75 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
54	RFCN			47.61 %	62.17 %	43.74 %	0.2 s	4 cores @ 2.5 Ghz (Python)
55	CLF3D			47.53 %	65.31 %	40.23 %	0.13 s	GPU @ 2.5 Ghz (Python)
56	RFCN_RFB			45.36 %	59.49 %	41.63 %	0.2 s	4 cores @ 2.5 Ghz (Python)
57	FD2			44.29 %	62.32 %	40.65 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
58	SeRC			44.28 %	55.81 %	38.50 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
59	Cmerge			43.85 %	61.60 %	42.60 %	0.2 s	4 cores @ 2.5 Ghz (Python)
60	MTDP			43.08 %	54.53 %	38.79 %	0.15 s	GPU @ 2.0 Ghz (Python)
61	GNN			42.65 %	59.43 %	37.72 %	0.2 s	1 core @ 2.5 Ghz (Python)
62	MV-RGBD-RF			42.61 %	51.46 %	37.42 %	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.
63	YOLOv3+d			42.60 %	59.08 %	40.77 %	0.04 s	GPU @ 1.5 Ghz (C/C++)
64	SS3D			40.96 %	56.34 %	35.17 %	88 ms	Tesla V100 (Python)
65	pAUCEnsT			37.88 %	52.28 %	33.38 %	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.
66	Retinanet100			37.54 %	46.39 %	30.82 %	0.2 s	4 cores @ 2.5 Ghz (Python)
67	softyolo			31.30 %	45.16 %	27.38 %	0.16 s	4 cores @ 2.5 Ghz (Python)
68	Vote3D			31.24 %	41.45 %	28.60 %	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.
69	DPM-VOC+VP			31.16 %	43.65 %	28.29 %	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.
70	LSVM-MDPM-us		code	30.81 %	40.31 %	28.17 %	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.
71	100Frcnn			29.95 %	44.60 %	27.70 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
72	LSVM-MDPM-sv			29.24 %	37.71 %	27.52 %	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.
73	DPM-C8B1			29.04 %	43.28 %	26.20 %	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.
74	R-CNN_VGG			28.79 %	37.71 %	25.82 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
75	rpn			28.65 %	37.40 %	23.50 %	0.01 s	1 core @ 2.5 Ghz (Python)
76	Lidar_ROI+Yolo(UJS)			27.21 %	39.41 %	26.12 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
77	mBoW			21.62 %	28.19 %	20.93 %	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.
78	DT3D			20.65 %	31.29 %	20.73 %	0,21s	GPU @ 2.5 Ghz (Python)
79	TopNet-HighRes			19.15 %	29.34 %	19.69 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
80	KD53-20			17.71 %	23.15 %	17.30 %	0.19 s	4 cores @ 2.5 Ghz (Python)
81	SA-3D			15.82 %	20.11 %	12.83 %	0.3 s	1 core @ 2.5 Ghz (Python)
82	Kyolo3			9.09 %	9.09 %	9.09 %	0.16 s	4 cores @ 2.5 Ghz (Python)
83	Fast-SSD			7.10 %	11.77 %	7.23 %	0.06 s	GTX650Ti
84	KD45			5.87 %	5.96 %	4.53 %	0.16 s	4 cores @ 2.5 Ghz (Python)
85	YOLOv2		code	4.55 %	4.55 %	4.55 %	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.
86	TopNet-DecayRate			1.01 %	0.04 %	1.01 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
87	softretina			0.44 %	0.29 %	0.22 %	0.16 s	4 cores @ 2.5 Ghz (Python)
88	JSyolo			0.22 %	0.22 %	0.22 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Cars

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	MVRA + I-FRCNN+			89.93 %	90.60 %	79.78 %	0.18 s	GPU @ 2.5 Ghz (Python)
2	Deep MANTA			89.86 %	97.19 %	80.39 %	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.
3	PointRCNN			89.55 %	90.76 %	80.76 %	0.2 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. arXiv preprint arXiv:1812.04244 2018.
4	PointPillars			88.76 %	90.19 %	86.38 %	16 ms	1080ti GPU and Intel i7 CPU
5	Sogo_MM			88.72 %	90.67 %	78.95 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
6	Deep3DBox			88.56 %	90.39 %	77.17 %	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.
7	SubCNN			88.43 %	90.61 %	78.63 %	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.
8	LTT			87.90 %	89.83 %	80.72 %	0.4 s	1 core @ 3.5 Ghz (Python)
9	MonoPSR			87.83 %	89.88 %	70.48 %	0.2 s	GPU @ 3.5 Ghz (Python)
10	ARPNET			87.68 %	89.79 %	79.05 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
11	AVOD		code	87.46 %	89.59 %	79.54 %	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.
12	SeoulRobotics-HFD			87.34 %	89.88 %	79.32 %	0.05 s	GPU @ 2.0 Ghz (Python + C/C++)
13	AVOD-FPN		code	87.13 %	89.95 %	79.74 %	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.
14	DFD			87.01 %	89.72 %	78.98 %	0.05 s	GPU @ 2.0 Ghz (Python + C/C++)
15	SECA			86.80 %	89.42 %	78.81 %	0.09 s	GPU @ 2.5 Ghz (Python)
16	SCANet			86.65 %	89.06 %	78.67 %	0.09s	GPU @ 2.5 Ghz (Python)
17	DeepStereoOP			86.57 %	89.01 %	77.13 %	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.
18	ELLIOT			86.13 %	89.69 %	80.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
19	Mono3D		code	85.83 %	89.00 %	76.00 %	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.
20	3DOP		code	85.81 %	88.56 %	76.21 %	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.
21	MBR-SSD			85.03 %	88.10 %	75.92 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
22	SECOND		code	81.31 %	87.84 %	71.95 %	0.05 s	4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
23	X_MD			80.28 %	89.53 %	79.14 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
24	FNV1_Fusion			80.12 %	89.25 %	78.58 %	0.11 s	GPU @ 2.5 Ghz (Python)
25	FNV1_RPN			80.10 %	89.27 %	78.66 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
26	SS3D			79.77 %	89.18 %	69.99 %	88 ms	Tesla V100 (Python)
27	VSE			79.56 %	89.11 %	78.14 %	0.15 s	GPU @ 2.5 Ghz (Python)
28	Complexer-YOLO			79.08 %	87.97 %	78.75 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
29	FNV1			78.97 %	88.40 %	76.70 %	0.11 s	GPU @ 2.5 Ghz (Python)
30	FRCNN+Or		code	77.61 %	88.52 %	67.69 %	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.
31	CLF3D			76.50 %	84.35 %	67.12 %	0.13 s	GPU @ 2.5 Ghz (Python)
32	avodC			76.30 %	86.31 %	68.71 %	0.1 s	GPU @ 2.5 Ghz (Python)
33	3D FCN			75.71 %	85.46 %	68.19 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
34	3D-SSMFCNN		code	75.42 %	75.44 %	67.27 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
35	Pose-RCNN			75.35 %	88.78 %	61.47 %	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	GS3D			75.16 %	83.52 %	59.59 %	0.58 s	GPU @ 2.5 Ghz (C/C++)
37	3DVP		code	74.59 %	81.02 %	64.11 %	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.
38	SubCat		code	74.42 %	80.74 %	58.83 %	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.
39	BdCost+			73.15 %	82.12 %	58.29 %	TBD s	4 cores @ 2.5 Ghz (Matlab/C++)
40	ROI-10D			67.85 %	74.24 %	59.28 %	0.2 s	GPU @ 3.5 Ghz (Python)
41	MF3D			67.68 %	87.79 %	57.57 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
42	multi-task CNN			66.19 %	76.69 %	58.11 %	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.
43	BdCost48LDCF		code	66.01 %	77.10 %	50.35 %	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.
44	BdCost48-25C			65.25 %	77.59 %	50.68 %	4 s	1 core @ 2.5 Ghz (C/C++)
45	OC-DPM			64.88 %	74.66 %	52.24 %	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.
46	3DVSSD			64.72 %	77.22 %	57.56 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
47	DPM-VOC+VP			63.27 %	77.51 %	47.57 %	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.
48	AOG-View			62.25 %	77.37 %	50.44 %	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.
49	SAMME48LDCF		code	57.49 %	75.12 %	46.64 %	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.
50	LSVM-MDPM-sv			56.69 %	70.86 %	45.91 %	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.
51	RCN-resnet101			53.93 %	56.36 %	48.32 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
52	SAG-Net			53.29 %	57.92 %	47.73 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
53	VeloFCN			52.70 %	70.21 %	46.11 %	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 .
54	desNet			51.78 %	54.13 %	46.46 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
55	vfnet			50.96 %	53.53 %	45.91 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
56	DPM-C8B1			50.32 %	59.53 %	39.22 %	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.
57	VAT-Net			49.91 %	52.74 %	45.16 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
58	InNet			49.55 %	52.32 %	44.79 %	0.16 s	GPU @ 3.5 Ghz (Python + C/C++)
59	CNN-ds		code	48.12 %	46.39 %	38.40 %	0.05 s	1 core @ 2.5 Ghz (Python)
60	ODES		code	48.06 %	46.22 %	42.43 %	0.02 s	GPU @ 2.5 Ghz (Python)
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61	ReSqueeze			45.40 %	47.38 %	41.68 %	0.03 s	GPU @ >3.5 Ghz (Python)
62	sensekitti		code	44.56 %	47.06 %	41.50 %	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.
63	VCTNet			43.41 %	46.53 %	39.42 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
64	Resnet101Faster rcnn			42.62 %	49.41 %	38.21 %	1 s	1 core @ 2.5 Ghz (Python)
65	FD2			39.44 %	47.56 %	35.20 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
66	bin			37.23 %	41.94 %	32.65 %	15ms s	GPU @ >3.5 Ghz (Python)
67	IPOD			37.01 %	36.95 %	36.96 %	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. arXiv preprint arXiv:1812.05276 2018.
68	merge12-12			36.02 %	36.92 %	31.88 %	0.2 s	4 cores @ 2.5 Ghz (Python)
69	BirdNet			35.81 %	50.85 %	34.90 %	0.11 s	Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
70	cascadercnn			35.01 %	34.13 %	28.55 %	0.36 s	4 cores @ 2.5 Ghz (Python)
71	Cmerge			32.95 %	36.87 %	29.14 %	0.2 s	4 cores @ 2.5 Ghz (Python)
72	ZKNet			32.91 %	37.16 %	28.94 %	0.01 s	GPU @ 2.0 Ghz (Python)
73	softretina			32.90 %	37.63 %	28.73 %	0.16 s	4 cores @ 2.5 Ghz (Python)
74	Fast-SSD			32.90 %	40.88 %	29.21 %	0.06 s	GTX650Ti
75	Retinanet100			32.87 %	37.54 %	28.69 %	0.2 s	4 cores @ 2.5 Ghz (Python)
76	LPN			32.41 %	33.97 %	29.15 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
77	SceneNet			32.02 %	36.62 %	28.46 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
78	detectron		code	31.71 %	35.58 %	28.18 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
79	RFCN_RFB			31.47 %	33.70 %	27.02 %	0.2 s	4 cores @ 2.5 Ghz (Python)
80	MTDP			31.04 %	34.12 %	27.50 %	0.15 s	GPU @ 2.0 Ghz (Python)
81	AOG		code	30.81 %	34.05 %	24.86 %	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.
82	yolo800			30.47 %	31.53 %	27.01 %	0.13 s	4 cores @ 2.5 Ghz (Python)
83	RFCN			30.29 %	33.30 %	25.44 %	0.2 s	4 cores @ 2.5 Ghz (Python)
84	Fast-SSD			29.60 %	40.48 %	25.85 %	0.06 s	GTX650Ti
85	fasterrcnn			28.13 %	29.83 %	24.76 %	0.2 s	4 cores @ 2.5 Ghz (Python)
86	VoxelNet(Unofficial)			27.26 %	26.86 %	25.12 %	0.5 s	GPU @ 2.0 Ghz (Python)
87	SubCat48LDCF		code	26.78 %	34.43 %	19.46 %	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.
88	RFBnet			26.39 %	32.45 %	23.97 %	0.2 s	4 cores @ 2.5 Ghz (Python)
89	Lidar_ROI+Yolo(UJS)			25.40 %	28.93 %	22.51 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
90	CSoR		code	25.38 %	34.43 %	21.95 %	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.
91	100Frcnn			25.26 %	34.82 %	21.73 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
92	RT3D			18.98 %	24.23 %	20.56 %	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.
93	softyolo			18.22 %	25.50 %	15.97 %	0.16 s	4 cores @ 2.5 Ghz (Python)
94	Kyolo3			18.21 %	19.50 %	15.99 %	0.16 s	4 cores @ 2.5 Ghz (Python)
95	VoxelNet basic			18.12 %	18.64 %	16.65 %	0.07 s	GPU (Python)
96	rpn			17.04 %	25.68 %	13.96 %	0.01 s	1 core @ 2.5 Ghz (Python)
97	KD45			16.04 %	16.89 %	14.96 %	0.16 s	4 cores @ 2.5 Ghz (Python)
98	Licar			15.58 %	18.24 %	16.15 %	0.09 s	GPU @ 2.0 Ghz (Python)
99	KD53-20			14.27 %	20.79 %	12.61 %	0.19 s	4 cores @ 2.5 Ghz (Python)
100	DLnet			8.48 %	9.09 %	7.39 %	0.3 s	4 cores @ 2.5 Ghz (C/C++)
101	SN-net			0.00 %	0.00 %	0.00 %	0.8 s	GPU @ 2.5 Ghz (Python + C/C++)
102	JSyolo			0.00 %	0.00 %	0.00 %	0.16 s	4 cores @ 2.5 Ghz (Python)