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	iDST-VC			90.55 %	90.88 %	81.04 %	4 s	GPU @ 2.5 Ghz (Python + C/C++)
2	BM-NET			90.48 %	90.83 %	80.63 %	4.0 s	GPU @ 2.5 Ghz (C/C++)
3	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.
4	THU CV-AI			90.31 %	90.75 %	72.20 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
5	RRC		code	90.22 %	90.61 %	87.44 %	3.6 s	GPU @ 2.5 Ghz (Python + 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.
6	SJTU-HW			90.08 %	90.81 %	79.98 %	0.85 s	GPU @ 1.5 Ghz (Python + C/C++)
7	SWC			90.05 %	90.82 %	80.59 %	0.5 s	GPU @ >3.5 Ghz (Python + C/C++)
8	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.
9	lpm			90.03 %	90.75 %	80.99 %	1 s	4 cores @ 3.5 Ghz (C/C++)
10	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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
11	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.
12	SAITv2			89.91 %	95.04 %	79.91 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
13	SG			89.89 %	90.51 %	80.67 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
14	M3D			89.88 %	90.59 %	80.61 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
15	CNN			89.81 %	90.50 %	80.64 %	1 s	1 core @ 2.5 Ghz (C/C++)
16	SINet+			89.73 %	90.51 %	77.82 %	0.3 s	GPU @ 2.5 Ghz (Matlab + C/C++)
17	Aston-EAS			89.64 %	90.49 %	77.95 %	0.24 s	8 cores @ >3.5 Ghz (Python + C/C++)
18	SINet_VGG			89.56 %	90.60 %	78.19 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
19	DF-PC_CNN			89.45 %	90.78 %	82.77 %	0.5 s	GPU @ 3.0 Ghz (Matlab + C/C++)
20	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.
21	RaC			89.39 %	90.02 %	80.29 %	1s s	GPU @ 1.0 Ghz (C/C++)
22	VCTNet			89.23 %	89.82 %	79.92 %	0.18 s	GPU @ 3.5 GHz (C/C++)
23	wt			89.17 %	90.80 %	79.58 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
24	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.
25	VAST-NET			89.11 %	90.53 %	79.63 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
26	SINet_PVA			89.08 %	90.44 %	75.85 %	0.11 s	GPU @ 2.5 Ghz (Matlab + C/C++)
27	HSR2			88.98 %	90.76 %	78.62 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
28	R-DML			88.92 %	90.42 %	79.57 %	0.16 s	GPU @ 2.5 Ghz (Python + C/C++)
29	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.
30	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.
31	FMLA			88.83 %	90.45 %	77.04 %	0.17 s	GPU @ 1.5 Ghz (C/C++)
32	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.
33	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.
34	RCNN			88.36 %	89.74 %	72.64 %	1 s	1 core @ 2.5 Ghz (C/C++)
35	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.
36	HM3D			88.26 %	89.86 %	78.24 %	0.35 s	GPU @ >3.5 Ghz (C/C++)
37	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.
38	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. arXiv preprint arXiv:1712.02294 2017.
39	VAT-Net			88.07 %	87.72 %	78.91 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
40	rcnInception			88.00 %	87.60 %	78.85 %	0.2 s	GPU @ 3.5 Ghz (Python + C/C++)
41	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.
42	WRInception			87.62 %	88.98 %	77.52 %	0.06 s	GPU @ 2.5 Ghz (C/C++)
43	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. arXiv preprint arXiv:1712.02294 2017.
44	vast-net			87.35 %	87.56 %	78.32 %	0.3 s	GPU @ 3.5 Ghz (Python)
45	MonoFusion			87.33 %	90.43 %	76.78 %	0.12 s	TITAN X GPU
46	RCL-FC			86.56 %	90.25 %	71.26 %	0.19 s	GPU @ 2.5 Ghz (Matlab + C/C++)
47	VxNet(LiDAR)			85.95 %	90.30 %	79.21 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
48	ReSqueeze			85.74 %	87.12 %	77.02 %	0.03 s	GPU @ >3.5 Ghz (Python)
49	AVOD-SSD		code	85.71 %	88.94 %	78.05 %	0.09 s	GPU @ 2.5 Ghz (Python)
50	YOLOv2-3cls			85.65 %	88.01 %	74.16 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
51	MMOD+CNN		code	83.14 %	89.86 %	69.29 %	0.28 s	4 cores @ >3.5 Ghz (C/C++)
52	R-RRC			82.94 %	89.99 %	72.21 %	0.09 s	GPU @ 1.0 Ghz (Python + C/C++)
53	LPN			81.67 %	87.70 %	72.69 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
54	A3DODWTDA (image)			81.54 %	76.21 %	66.85 %	0.8 s	GPU @ 3.0 Ghz (Python)
55	ISSD			81.39 %	88.08 %	72.94 %	0.31 s	GPU @ 3.0 Ghz (Python + C/C++)
56	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.
57	deprecated			81.29 %	85.23 %	69.32 %	0.00 s	GPU @ 2.5 Ghz (C/C++)
58	Fast R-RRC			80.80 %	89.23 %	71.47 %	0.058 s	GPU @ 1.0 Ghz (Python + C/C++)
59	V-net			80.35 %	75.36 %	73.76 %	0.3 s	1 core @ 3.5 Ghz (Python + C/C++)
60	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.
61	FPN			79.48 %	89.45 %	69.81 %	5 s	1 core @ 2.5 Ghz (C/C++)
62	RFCN			79.44 %	88.69 %	70.06 %	0.3 s	GPU @ 1.0 Ghz (Python + C/C++)
63	Denet			79.30 %	88.42 %	69.92 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
64	rtd			79.23 %	87.81 %	69.52 %	0.01 s	1 core @ 2.5 Ghz (Python)
65	RefineNet			79.21 %	90.16 %	65.71 %	0.20 s	GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
66	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.
67	FRCNN+Or		code	78.95 %	89.87 %	68.97 %	0.09 s	Titan Xp GPU
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.
68	fd3			78.91 %	86.73 %	70.29 %	0.01 s	GPU @ 2.5 Ghz (C/C++)
69	MB-Net			77.91 %	86.31 %	61.22 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
70	HM			77.72 %	87.90 %	61.36 %	1 s	1 core @ 2.5 Ghz (C/C++)
71	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.
72	SceneNet			77.34 %	87.90 %	68.38 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
73	MTDP			76.91 %	84.24 %	67.91 %	0.15 s	GPU @ 2.0 Ghz (Python)
74	FYSqueeze			76.73 %	84.06 %	67.96 %	0.01 s	>8 cores @ 2.5 Ghz (Python)
75	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.
76	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.
77	Inha_cvlab			76.04 %	84.38 %	67.32 %	0.01 s	GPU @ 2.5 Ghz (Python)
78	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.
79	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.
80	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.
81	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.
82	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.
83	AR-FCN			75.49 %	81.24 %	66.00 %	0.19 s	GPU @ 2.5 Ghz (C/C++)
84	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.
85	Roadstar.ai			74.84 %	82.93 %	67.18 %	0.08 s	GPU @ 2.0 Ghz (Python)
86	A3DODWTDA			74.71 %	78.21 %	66.70 %	0.08 s	GPU @ 3.0 Ghz (Python)
87	FD2			74.68 %	87.14 %	65.70 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
88	3dSSD			74.53 %	83.54 %	67.59 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
89	CHTTL			73.54 %	80.68 %	65.43 %	0.07 s	1 core @ 2.5 Ghz (Python)
90	3DVSSD			73.39 %	84.39 %	65.64 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
91	FD			72.64 %	82.34 %	60.31 %	0.01 s	GPU @ >3.5 Ghz (Python)
92	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.
93	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.
94	YOLOv2		code	69.01 %	86.40 %	59.57 %	0.03 s	GPU @ 2.0 Ghz (Python + C/C++)
95	tester			68.85 %	78.94 %	62.32 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
96	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.
97	GPVL			67.89 %	77.76 %	58.23 %	10 s	1 core @ 2.5 Ghz (C/C++)
98	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.
99	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.
100	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.
101	HNet		code	66.00 %	77.09 %	53.89 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
102	BdCost48-25C			65.95 %	78.21 %	51.23 %	4 s	1 core @ 2.5 Ghz (C/C++)
103	bin			64.39 %	77.58 %	56.33 %	15ms s	GPU @ >3.5 Ghz (Python)
104	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.
105	BNet			63.24 %	75.09 %	56.05 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
106	SN-net			63.11 %	80.38 %	55.60 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
107	GNN			62.59 %	76.03 %	50.18 %	0.2 s	1 core @ 2.5 Ghz (Python)
108	NMRDO			61.72 %	79.48 %	54.06 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
109	YOLO9000		code	61.31 %	76.79 %	50.25 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: better, faster, stronger. arXiv preprint 2016.
110	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.
111	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.
112	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.
113	GNN			58.29 %	76.26 %	49.96 %	0.2 s	1 core @ 2.5 Ghz (Python)
114	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.
115	Faster RCNN			56.58 %	62.31 %	45.27 %	0.11 s	GPU @ 2.5 Ghz (Python)
116	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.
117	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.
118	FRO			53.78 %	70.96 %	46.00 %	0.19 s	GPU @ 2.5 Ghz (Python)
119	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 .
120	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). .
121	SDN			52.03 %	71.75 %	47.08 %	0.096 s	GPU @ 1.7 Ghz (Python)
122	F-PC_CNN			48.61 %	65.73 %	47.67 %	0.5 s	GPU @ 3.0 Ghz (Matlab + C/C++)
X. Du, M. Jr., S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018.
123	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.
124	LMNetV2			44.20 %	59.58 %	37.90 %	0.02 s	GPU @ 2.5 Ghz (C/C++)
125	LiCar			40.05 %	50.23 %	41.80 %	0.09 s	GPU @ 2.5 Ghz (Python)
126	LMnetV1.1			36.88 %	53.16 %	30.47 %	0.01 s	GPU @ 1.0 Ghz (Python + C/C++)
127	LMnet			36.06 %	51.10 %	30.09 %	0.013 s	GPU @ 1.1 Ghz (Python + C/C++)
128	YOLO			35.86 %	49.47 %	29.74 %	0.03 s	GPU @ 1.0 Ghz (C/C++)
129	DoBEM			33.61 %	36.35 %	37.78 %	0.6 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Yu, T. Westfechtel, R. Hamada, K. Ohno and S. Tadokoro: Vehicle Detection and Localization on Bird's Eye View Elevation Images Using Convolutional Neural Network. IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) 2017.
130	fastRand		code	27.83 %	35.24 %	22.33 %	0.05 s	1 core @ 2.5 Ghz (Matlab + C/C++)
131	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.
132	R-CNN_VGG			26.04 %	32.23 %	20.93 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
133	FCN-Depth		code	25.66 %	50.55 %	24.95 %	1 s	GPU @ 1.5 Ghz (Matlab + C/C++)
134	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.
135	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.
136	SPC			18.83 %	25.30 %	17.29 %	0.4 s	4 cores @ 2.5 Ghz (Python)
137	LidarNet			2.66 %	1.98 %	2.18 %	0.007 s	GPU @ 2.5 Ghz (C/C++)

Table as LaTeX | Only published Methods

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	iDST-VC			80.90 %	88.13 %	74.08 %	1.5 s	GPU @ 2.5 Ghz (Python + C/C++)
2	SWC			78.65 %	87.06 %	73.92 %	0.5 s	GPU @ >3.5 Ghz (Python + C/C++)
3	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.
4	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.
5	VCTNet			75.88 %	84.03 %	71.60 %	0.18 s	GPU @ 3.5 GHz (C/C++)
6	Argus_detection_v1			75.51 %	83.49 %	71.24 %	0.25 s	GPU @ 1.5 Ghz (C/C++)
7	SiRtAKI			75.47 %	86.54 %	68.27 %	0.18 s	GPU @ >3.5 Ghz (C/C++)
8	RRC		code	75.33 %	84.14 %	70.39 %	3.6 s	GPU @ 2.5 Ghz (Python + 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.
9	iFDT			74.83 %	86.02 %	70.55 %	2.4 s	GPU @ 2.5 Ghz (Python + C/C++)
10	Aston-EAS			74.52 %	85.12 %	69.35 %	0.24 s	8 cores @ >3.5 Ghz (Python + C/C++)
11	SJTU-HW			74.24 %	85.42 %	69.34 %	0.85 s	GPU @ 1.5 Ghz (Python + C/C++)
12	FMLA			73.75 %	83.86 %	68.06 %	0.17 s	GPU @ 1.5 Ghz (C/C++)
13	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.
14	LFF			73.04 %	82.91 %	67.77 %	1 s	GPU
15	RCL-FC			72.78 %	82.75 %	67.53 %	0.19 s	GPU @ 2.5 Ghz (Matlab + C/C++)
16	FRCNN			72.64 %	82.52 %	69.21 %	1 s	>8 cores @ 2.5 Ghz (Python + C/C++)
17	wt			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	WRInception			68.76 %	79.98 %	63.48 %	0.06 s	GPU @ 2.5 Ghz (C/C++)
24	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.
25	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.
26	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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
27	VAT-Net			67.15 %	77.28 %	59.59 %	0.08 s	GPU @ 2.5 Ghz (Python)
28	HNet		code	66.74 %	77.39 %	62.26 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
29	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.
30	HM3D			65.97 %	77.60 %	61.09 %	0.35 s	GPU @ >3.5 Ghz (C/C++)
31	HSR2			65.91 %	78.05 %	63.05 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
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	R-DML			64.82 %	77.15 %	60.76 %	0.16 s	GPU @ 2.5 Ghz (Python + C/C++)
34	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.
35	PCN			63.48 %	77.88 %	58.59 %	0.6 s
36	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.
37	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.
38	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.
39	ReSqueeze			61.25 %	72.78 %	57.43 %	0.03 s	GPU @ >3.5 Ghz (Python)
40	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.
41	V-net			60.04 %	76.90 %	52.15 %	0.3 s	1 core @ 3.5 Ghz (Python + C/C++)
42	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.
43	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.
44	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. arXiv preprint arXiv:1712.02294 2017.
45	LPN			58.18 %	70.54 %	54.18 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
46	RFCN			58.06 %	74.44 %	51.14 %	0.3 s	GPU @ 1.0 Ghz (Python + C/C++)
47	ACNet+BRFRes			57.23 %	67.60 %	51.79 %	0.55 s	1 core @ 2.5 Ghz (Matlab)
48	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.
49	p2dv			56.98 %	68.71 %	50.99 %	1 s	1 core @ 2.5 Ghz (C/C++)
50	FRCNN+Or		code	56.78 %	71.18 %	52.86 %	0.09 s	Titan Xp GPU
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.
51	D-TSF			56.77 %	69.03 %	50.77 %	1 s	1 core @ 2.5 Ghz (C/C++)
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52	FD2			56.68 %	71.09 %	51.65 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
53	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.
54	YOLOv2-3cls			55.43 %	70.05 %	51.55 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
55	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.
56	FD			55.33 %	67.87 %	50.02 %	0.01 s	GPU @ >3.5 Ghz (Python)
57	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.
58	deprecated			54.02 %	70.43 %	49.83 %	0.00 s	GPU @ 2.5 Ghz (C/C++)
59	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.
60	CHTTL			53.04 %	66.26 %	49.48 %	0.07 s	1 core @ 2.5 Ghz (Python)
61	MTDP			52.97 %	66.97 %	47.64 %	0.15 s	GPU @ 2.0 Ghz (Python)
62	FYSqueeze			52.60 %	66.07 %	48.40 %	0.01 s	>8 cores @ 2.5 Ghz (Python)
63	HM			51.89 %	68.95 %	43.86 %	1 s	1 core @ 2.5 Ghz (C/C++)
64	Inha_cvlab			51.13 %	63.59 %	46.77 %	0.01 s	GPU @ 2.5 Ghz (Python)
65	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.
66	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.
67	SSD1			50.14 %	63.93 %	47.46 %	0.255 s	GPU @ 2.5 Ghz (python+ C/C++)
68	fd3			47.67 %	59.28 %	44.46 %	0.01 s	GPU @ 2.5 Ghz (C/C++)
69	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.
70	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.
71	FSSPD			46.39 %	60.66 %	43.44 %	0.07 s	GPU @ 2.0 Ghz (Python + C/C++)
72	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.
73	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.
74	FRO			45.43 %	57.56 %	40.50 %	0.19 s	GPU @ 2.5 Ghz (Python)
75	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.
76	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.
77	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.
78	LXT-DET			44.18 %	61.27 %	43.54 %	0.1 s	1 core @ 2.5 Ghz (Python)
79	VxNet(LiDAR)			44.08 %	50.61 %	42.84 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
80	AR-FCN			43.88 %	53.16 %	35.58 %	0.19 s	GPU @ 2.5 Ghz (C/C++)
81	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. arXiv preprint arXiv:1712.02294 2017.
82	YOLOv2		code	43.33 %	53.02 %	35.41 %	0.03 s	GPU @ 2.0 Ghz (Python + C/C++)
83	Roadstar.ai			42.72 %	47.02 %	42.36 %	0.08 s	GPU @ 2.0 Ghz (Python)
84	GNN			42.56 %	58.22 %	40.53 %	0.2 s	1 core @ 2.5 Ghz (Python)
85	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.
86	bin			40.91 %	55.95 %	39.05 %	15ms s	GPU @ >3.5 Ghz (Python)
87	GNN			40.69 %	55.22 %	38.65 %	0.2 s	1 core @ 2.5 Ghz (Python)
88	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). .
89	NMRDO			40.59 %	55.43 %	39.75 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
90	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.
91	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.
92	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.
93	3dSSD			34.86 %	44.59 %	34.77 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
94	BNet			34.40 %	41.05 %	28.88 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
95	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.
96	SN-net			31.15 %	35.42 %	26.57 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
97	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.
98	YOLO			24.35 %	25.63 %	17.50 %	0.03 s	GPU @ 1.0 Ghz (C/C++)
99	R-CNN_VGG			23.16 %	28.95 %	22.17 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
100	LMNetV2			23.00 %	27.03 %	23.26 %	0.02 s	GPU @ 2.5 Ghz (C/C++)
101	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.
102	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.
103	LMnetV1.1			6.96 %	7.96 %	6.94 %	0.01 s	GPU @ 1.0 Ghz (Python + C/C++)
104	LMnet			6.50 %	7.34 %	6.57 %	0.013 s	GPU @ 1.1 Ghz (Python + C/C++)

Table as LaTeX | Only published Methods

Cyclist

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	iDST-VT			78.21 %	86.06 %	69.47 %	1 s	GPU @ 2.5 Ghz (C/C++)
2	SWC			77.58 %	86.02 %	68.44 %	0.5 s	GPU @ >3.5 Ghz (Python + C/C++)
3	VCTNet			77.20 %	83.41 %	68.78 %	0.18 s	GPU @ 3.5 GHz (C/C++)
4	RRC		code	76.47 %	84.96 %	65.46 %	3.6 s	GPU @ 2.5 Ghz (Python + 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.
5	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.
6	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.
7	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.
8	SiRtAKI			73.34 %	84.59 %	64.72 %	0.18 s	GPU @ >3.5 Ghz (C/C++)
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	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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
11	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.
12	RCL-FC			72.01 %	79.77 %	63.31 %	0.19 s	GPU @ 2.5 Ghz (Matlab + C/C++)
13	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.
14	wt			70.72 %	77.57 %	62.23 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
15	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.
16	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.
17	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.
18	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.
19	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.
20	HSR2			64.94 %	76.36 %	57.62 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
21	Roadstar.ai			64.48 %	75.74 %	57.79 %	0.08 s	GPU @ 2.0 Ghz (Python)
22	R-DML			63.90 %	76.60 %	56.98 %	0.16 s	GPU @ 2.5 Ghz (Python + C/C++)
23	HM3D			63.89 %	76.28 %	56.51 %	0.35 s	GPU @ >3.5 Ghz (C/C++)
24	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.
25	WRInception			62.85 %	78.19 %	55.64 %	0.06 s	GPU @ 2.5 Ghz (C/C++)
26	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.
27	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.
28	VxNet(LiDAR)			59.33 %	72.04 %	54.72 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
29	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. arXiv preprint arXiv:1712.02294 2017.
30	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.
31	FRCNN+Or		code	57.37 %	70.05 %	51.00 %	0.09 s	Titan Xp GPU
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.
32	VAT-Net			57.01 %	68.71 %	50.14 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
33	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. arXiv preprint arXiv:1712.02294 2017.
34	ReSqueeze			54.93 %	68.34 %	49.19 %	0.03 s	GPU @ >3.5 Ghz (Python)
35	HNet		code	54.10 %	69.71 %	48.02 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
36	deprecated			52.95 %	69.91 %	46.80 %	0.00 s	GPU @ 2.5 Ghz (C/C++)
37	YOLOv2-3cls			51.67 %	68.14 %	45.79 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
38	RFCN			51.19 %	63.26 %	44.54 %	0.3 s	GPU @ 1.0 Ghz (Python + C/C++)
39	LPN			50.02 %	65.33 %	44.85 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
40	FYSqueeze			48.80 %	67.03 %	43.82 %	0.01 s	>8 cores @ 2.5 Ghz (Python)
41	V-net			48.49 %	65.74 %	46.94 %	0.3 s	1 core @ 3.5 Ghz (Python + C/C++)
42	CHTTL			48.28 %	64.06 %	43.07 %	0.07 s	1 core @ 2.5 Ghz (Python)
43	FD2			44.29 %	62.32 %	40.65 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
44	MTDP			43.08 %	54.53 %	38.79 %	0.15 s	GPU @ 2.0 Ghz (Python)
45	HM			42.99 %	60.32 %	41.45 %	1 s	1 core @ 2.5 Ghz (C/C++)
46	FRO			42.98 %	59.96 %	38.97 %	0.19 s	GPU @ 2.5 Ghz (Python)
47	GNN			42.65 %	59.43 %	37.72 %	0.2 s	1 core @ 2.5 Ghz (Python)
48	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.
49	Inha_cvlab			42.39 %	60.04 %	38.26 %	0.01 s	GPU @ 2.5 Ghz (Python)
50	AR-FCN			41.83 %	51.05 %	33.99 %	0.19 s	GPU @ 2.5 Ghz (C/C++)
51	YOLOv2		code	39.96 %	56.59 %	33.06 %	0.03 s	GPU @ 2.0 Ghz (Python + C/C++)
52	BNet			38.07 %	54.91 %	30.98 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
53	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.
54	GNN			37.64 %	54.47 %	35.09 %	0.2 s	1 core @ 2.5 Ghz (Python)
55	FD			37.01 %	51.41 %	32.93 %	0.01 s	GPU @ >3.5 Ghz (Python)
56	SN-net			35.02 %	50.98 %	29.26 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
57	3dSSD			34.00 %	42.51 %	32.04 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
58	NMRDO			33.43 %	46.39 %	27.79 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
59	bin			31.60 %	44.33 %	28.12 %	15ms s	GPU @ >3.5 Ghz (Python)
60	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.
61	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.
62	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.
63	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.
64	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.
65	R-CNN_VGG			28.79 %	37.71 %	25.82 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
66	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.
67	YOLO			13.96 %	18.07 %	13.83 %	0.03 s	GPU @ 1.0 Ghz (C/C++)
68	LMNetV2			12.80 %	18.67 %	12.43 %	0.02 s	GPU @ 2.5 Ghz (C/C++)
69	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.
70	LMnetV1.1			2.61 %	1.91 %	2.73 %	0.01 s	GPU @ 1.0 Ghz (Python + C/C++)
71	LMnet			1.73 %	2.13 %	1.99 %	0.013 s	GPU @ 1.1 Ghz (Python + C/C++)

Table as LaTeX | Only published Methods

Cars

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	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.
2	RaC			89.25 %	89.98 %	80.07 %	1s s	GPU @ 1.0 Ghz (C/C++)
3	M3D			89.23 %	90.41 %	79.60 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
4	wt			88.72 %	90.67 %	78.95 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
5	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.
6	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.
7	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. arXiv preprint arXiv:1712.02294 2017.
8	HM3D			87.29 %	89.41 %	77.08 %	0.35 s	GPU @ >3.5 Ghz (C/C++)
9	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. arXiv preprint arXiv:1712.02294 2017.
10	MonoFusion			87.03 %	90.35 %	76.37 %	0.12 s	TITAN X GPU
11	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.
12	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.
13	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.
14	AVOD-SSD		code	85.05 %	88.69 %	77.35 %	0.09 s	GPU @ 2.5 Ghz (Python)
15	FRCNN+Or		code	77.61 %	88.52 %	67.69 %	0.09 s	Titan Xp GPU
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.
16	MB-Net			76.12 %	85.38 %	59.84 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
17	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.
18	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.
19	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.
20	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.
21	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.
22	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.
23	BdCost48-25C			65.25 %	77.59 %	50.68 %	4 s	1 core @ 2.5 Ghz (C/C++)
24	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.
25	3DVSSD			64.72 %	77.22 %	57.56 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
26	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.
27	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.
28	NMRDO			59.55 %	77.38 %	51.91 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
29	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.
30	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.
31	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 .
32	VAST-NET			51.79 %	53.92 %	46.44 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
33	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.
34	VAT-Net			49.79 %	51.73 %	45.00 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
35	rcnInception			49.63 %	51.71 %	44.89 %	0.2 s	GPU @ 3.5 Ghz (Python + C/C++)
36	vast-net			49.43 %	51.85 %	44.99 %	0.3 s	GPU @ 3.5 Ghz (Python)
37	HSR2			45.46 %	47.03 %	40.60 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
38	ReSqueeze			45.40 %	47.38 %	41.68 %	0.03 s	GPU @ >3.5 Ghz (Python)
39	WRInception			45.07 %	47.05 %	40.52 %	0.06 s	GPU @ 2.5 Ghz (C/C++)
40	VCTNet			44.78 %	48.36 %	40.04 %	0.18 s	GPU @ 3.5 GHz (C/C++)
41	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. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
42	V-net			44.33 %	44.39 %	41.36 %	0.3 s	1 core @ 3.5 Ghz (Python + C/C++)
43	LMNetV2			43.40 %	58.86 %	37.15 %	0.02 s	GPU @ 2.5 Ghz (C/C++)
44	fd3			41.85 %	47.02 %	37.77 %	0.01 s	GPU @ 2.5 Ghz (C/C++)
45	Inha_cvlab			41.63 %	46.83 %	37.38 %	0.01 s	GPU @ 2.5 Ghz (Python)
46	FD			40.40 %	46.30 %	34.01 %	0.01 s	GPU @ >3.5 Ghz (Python)
47	FD2			39.44 %	47.56 %	35.20 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
48	LMnetV1.1			36.66 %	52.94 %	30.28 %	0.01 s	GPU @ 1.0 Ghz (Python + C/C++)
49	LMnet			35.86 %	50.81 %	29.90 %	0.013 s	GPU @ 1.1 Ghz (Python + C/C++)
50	SN-net			35.62 %	46.08 %	31.71 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
51	SDN			34.48 %	47.13 %	31.40 %	0.096 s	GPU @ 1.7 Ghz (Python)
52	YOLOv2-3cls			34.10 %	35.61 %	30.21 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
53	MMOD+CNN		code	33.90 %	36.43 %	28.49 %	0.28 s	4 cores @ >3.5 Ghz (C/C++)
54	FYSqueeze			33.84 %	33.19 %	30.65 %	0.01 s	>8 cores @ 2.5 Ghz (Python)
55	RFCN			33.59 %	37.75 %	29.82 %	0.3 s	GPU @ 1.0 Ghz (Python + C/C++)
56	deprecated			32.72 %	34.26 %	28.06 %	0.00 s	GPU @ 2.5 Ghz (C/C++)
57	LPN			32.41 %	33.97 %	29.15 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
58	SceneNet			32.02 %	36.62 %	28.46 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
59	MTDP			31.04 %	34.12 %	27.50 %	0.15 s	GPU @ 2.0 Ghz (Python)
60	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.
61	Roadstar.ai			29.44 %	32.87 %	26.77 %	0.08 s	GPU @ 2.0 Ghz (Python)
62	CHTTL			29.30 %	32.38 %	26.44 %	0.07 s	1 core @ 2.5 Ghz (Python)
63	YOLOv2		code	26.98 %	34.61 %	23.42 %	0.03 s	GPU @ 2.0 Ghz (Python + C/C++)
64	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.
65	bin			25.79 %	31.13 %	22.90 %	15ms s	GPU @ >3.5 Ghz (Python)
66	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.
67	LiCar			19.16 %	24.40 %	20.80 %	0.09 s	GPU @ 2.5 Ghz (Python)
68	DoBEM			14.02 %	15.35 %	16.33 %	0.6 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Yu, T. Westfechtel, R. Hamada, K. Ohno and S. Tadokoro: Vehicle Detection and Localization on Bird's Eye View Elevation Images Using Convolutional Neural Network. IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) 2017.
69	SPC			12.12 %	15.61 %	11.23 %	0.4 s	4 cores @ 2.5 Ghz (Python)
70	LidarNet			1.09 %	0.70 %	0.88 %	0.007 s	GPU @ 2.5 Ghz (C/C++)

Table as LaTeX | Only published Methods

Pedestrians

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	wt			66.83 %	78.89 %	62.06 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
2	SubCNN			66.28 %	78.33 %	61.37 %	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.
3	Pose-RCNN			59.89 %	74.10 %	54.21 %	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.
4	3DOP		code	59.79 %	73.46 %	57.04 %	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.
5	DeepStereoOP			59.28 %	73.37 %	56.87 %	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.
6	HM3D			58.21 %	70.22 %	53.72 %	0.35 s	GPU @ >3.5 Ghz (C/C++)
7	Mono3D		code	58.12 %	68.58 %	54.94 %	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.
8	FRCNN+Or		code	52.62 %	66.84 %	48.72 %	0.09 s	Titan Xp GPU
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.
9	AVOD-FPN		code	44.92 %	53.36 %	43.77 %	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. arXiv preprint arXiv:1712.02294 2017.
10	DPM-VOC+VP			39.83 %	53.66 %	35.73 %	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.
11	VCTNet			38.73 %	42.30 %	36.69 %	0.18 s	GPU @ 3.5 GHz (C/C++)
12	sensekitti		code	37.50 %	43.55 %	35.08 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
13	AVOD		code	36.38 %	44.12 %	31.81 %	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. arXiv preprint arXiv:1712.02294 2017.
14	LSVM-MDPM-sv			35.49 %	47.00 %	32.42 %	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.
15	RFCN			35.26 %	44.20 %	31.60 %	0.3 s	GPU @ 1.0 Ghz (Python + C/C++)
16	WRInception			35.14 %	40.34 %	32.50 %	0.06 s	GPU @ 2.5 Ghz (C/C++)
17	SubCat			34.18 %	43.95 %	30.76 %	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.
18	YOLOv2-3cls			33.97 %	42.38 %	31.83 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
19	HSR2			33.86 %	39.97 %	32.48 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
20	NMRDO			33.06 %	44.95 %	31.83 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
21	SSD1			32.73 %	41.73 %	30.69 %	0.255 s	GPU @ 2.5 Ghz (python+ C/C++)
22	FYSqueeze			32.66 %	40.20 %	30.25 %	0.01 s	>8 cores @ 2.5 Ghz (Python)
23	RPN+BF		code	32.55 %	40.97 %	29.52 %	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.
24	ReSqueeze			32.35 %	37.95 %	30.38 %	0.03 s	GPU @ >3.5 Ghz (Python)
25	LPN			31.63 %	38.40 %	28.90 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
26	VAT-Net			31.45 %	35.14 %	28.10 %	0.08 s	GPU @ 2.5 Ghz (Python)
27	deprecated			30.04 %	39.60 %	27.56 %	0.00 s	GPU @ 2.5 Ghz (C/C++)
28	MTDP			29.04 %	36.90 %	25.96 %	0.15 s	GPU @ 2.0 Ghz (Python)
29	CHTTL			29.01 %	36.41 %	26.95 %	0.07 s	1 core @ 2.5 Ghz (Python)
30	FD2			28.59 %	35.53 %	26.02 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
31	ACF			28.46 %	35.69 %	26.18 %	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.
32	V-net			28.37 %	35.28 %	24.76 %	0.3 s	1 core @ 3.5 Ghz (Python + C/C++)
33	FD			27.90 %	33.68 %	25.17 %	0.01 s	GPU @ >3.5 Ghz (Python)
34	YOLOv2		code	27.35 %	32.98 %	22.99 %	0.03 s	GPU @ 2.0 Ghz (Python + C/C++)
35	Inha_cvlab			26.96 %	33.08 %	24.74 %	0.01 s	GPU @ 2.5 Ghz (Python)
36	bin			26.22 %	34.76 %	25.12 %	15ms s	GPU @ >3.5 Ghz (Python)
37	fd3			25.38 %	31.30 %	24.18 %	0.01 s	GPU @ 2.5 Ghz (C/C++)
38	DPM-C8B1			23.37 %	31.08 %	20.72 %	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.
39	LXT-DET			23.22 %	30.35 %	23.00 %	0.1 s	1 core @ 2.5 Ghz (Python)
40	ACF-MR			23.18 %	29.35 %	21.00 %	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.
41	Roadstar.ai			22.18 %	24.51 %	21.91 %	0.08 s	GPU @ 2.0 Ghz (Python)
42	SN-net			16.66 %	18.64 %	14.46 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
43	LMNetV2			14.79 %	18.18 %	14.67 %	0.02 s	GPU @ 2.5 Ghz (C/C++)
44	LMnetV1.1			4.51 %	5.28 %	4.39 %	0.01 s	GPU @ 1.0 Ghz (Python + C/C++)
45	LMnet			4.13 %	4.71 %	4.09 %	0.013 s	GPU @ 1.1 Ghz (Python + C/C++)

Table as LaTeX | Only published Methods

Cyclists

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	wt			63.59 %	70.70 %	56.15 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
2	SubCNN			63.41 %	71.39 %	56.34 %	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.
3	Pose-RCNN			62.25 %	74.85 %	55.09 %	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.
4	Deep3DBox			59.37 %	68.58 %	51.97 %	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.
5	3DOP		code	58.59 %	71.95 %	52.35 %	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.
6	AVOD-FPN		code	57.53 %	67.61 %	54.16 %	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. arXiv preprint arXiv:1712.02294 2017.
7	DeepStereoOP			55.62 %	67.49 %	48.85 %	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.
8	HM3D			55.12 %	67.32 %	48.86 %	0.35 s	GPU @ >3.5 Ghz (C/C++)
9	AVOD		code	54.43 %	64.36 %	47.67 %	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. arXiv preprint arXiv:1712.02294 2017.
10	Mono3D		code	53.11 %	65.74 %	48.87 %	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.
11	FRCNN+Or		code	50.91 %	63.41 %	45.46 %	0.09 s	Titan Xp GPU
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.
12	VCTNet			43.14 %	48.22 %	38.67 %	0.18 s	GPU @ 3.5 GHz (C/C++)
13	sensekitti		code	42.12 %	46.65 %	36.66 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
14	HSR2			36.82 %	42.76 %	32.33 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
15	WRInception			34.02 %	41.88 %	29.37 %	0.06 s	GPU @ 2.5 Ghz (C/C++)
16	VAT-Net			32.94 %	40.36 %	28.04 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
17	V-net			27.67 %	38.92 %	26.24 %	0.3 s	1 core @ 3.5 Ghz (Python + C/C++)
18	Roadstar.ai			27.58 %	32.23 %	24.88 %	0.08 s	GPU @ 2.0 Ghz (Python)
19	ReSqueeze			27.40 %	35.39 %	24.32 %	0.03 s	GPU @ >3.5 Ghz (Python)
20	LPN			27.01 %	32.96 %	25.01 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
21	RFCN			26.92 %	32.01 %	23.86 %	0.3 s	GPU @ 1.0 Ghz (Python + C/C++)
22	FYSqueeze			26.71 %	34.53 %	24.39 %	0.01 s	>8 cores @ 2.5 Ghz (Python)
23	FD2			24.65 %	35.58 %	21.97 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
24	deprecated			24.05 %	31.01 %	21.12 %	0.00 s	GPU @ 2.5 Ghz (C/C++)
25	NMRDO			23.53 %	32.68 %	19.81 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
26	CHTTL			23.51 %	30.24 %	21.05 %	0.07 s	1 core @ 2.5 Ghz (Python)
27	Inha_cvlab			23.38 %	33.62 %	20.73 %	0.01 s	GPU @ 2.5 Ghz (Python)
28	DPM-VOC+VP			23.22 %	31.24 %	21.62 %	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.
29	LSVM-MDPM-sv			23.14 %	28.89 %	22.28 %	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.
30	YOLOv2		code	22.36 %	28.97 %	19.45 %	0.03 s	GPU @ 2.0 Ghz (Python + C/C++)
31	YOLOv2-3cls			22.03 %	28.27 %	19.80 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
32	FD			21.60 %	30.76 %	18.56 %	0.01 s	GPU @ >3.5 Ghz (Python)
33	DPM-C8B1			19.25 %	27.16 %	17.95 %	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.
34	MTDP			18.95 %	23.33 %	17.24 %	0.15 s	GPU @ 2.0 Ghz (Python)
35	SN-net			18.41 %	28.30 %	14.80 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
36	bin			12.64 %	17.86 %	11.33 %	15ms s	GPU @ >3.5 Ghz (Python)
37	LMNetV2			8.33 %	13.37 %	8.14 %	0.02 s	GPU @ 2.5 Ghz (C/C++)
38	LMnetV1.1			1.45 %	1.33 %	1.58 %	0.01 s	GPU @ 1.0 Ghz (Python + C/C++)
39	LMnet			1.16 %	1.61 %	1.39 %	0.013 s	GPU @ 1.1 Ghz (Python + C/C++)

Table as LaTeX | Only published Methods