\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
TuSimple & & 77.04 \% & 86.78 \% & 72.40 \% & 1.6 s / GPU & \\
RRC & & 75.33 \% & 84.14 \% & 70.39 \% & 3.6 s / GPU & 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.\\
iFDT & & 74.32 \% & 85.61 \% & 68.88 \% & 2.4 s / GPU & \\
Allspark & & 74.22 \% & 84.44 \% & 68.61 \% & 0.7 s / GPU & \\
TiCNN & & 74.07 \% & 84.00 \% & 68.50 \% & 0.5 s / GPU & \\
MS-CNN & & 73.62 \% & 83.70 \% & 68.28 \% & 0.4 s / GPU & Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.\\
Pie & & 73.17 \% & 84.25 \% & 67.55 \% & 1.2 s / 1 core & \\
SAIT & & 72.61 \% & 84.79 \% & 67.94 \% & 0.15 s / GPU & \\
uickitti & & 71.84 \% & 83.45 \% & 67.00 \% & 1.5 s / GPU & \\
GN & & 71.55 \% & 80.73 \% & 64.82 \% & 1 s / GPU & \\
SubCNN & & 71.34 \% & 83.17 \% & 66.36 \% & 2 s / GPU & 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.\\
IVA & & 70.63 \% & 83.03 \% & 64.68 \% & 0.4 s / GPU & 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.\\
SDP+RPN & & 70.20 \% & 79.98 \% & 64.84 \% & 0.4 s / GPU & 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.\\
MM-MRFC & fl la & 69.96 \% & 82.37 \% & 64.76 \% & 0.05 s / GPU & \\
WRInception & & 68.76 \% & 79.98 \% & 63.48 \% & 0.06 s / GPU & \\
3DOP & st & 67.46 \% & 82.36 \% & 64.71 \% & 3s / GPU & 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.\\
DeepStereoOP & & 67.32 \% & 82.50 \% & 65.14 \% & 3.4 s / GPU & 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.\\
sensekitti & & 67.28 \% & 80.12 \% & 62.25 \% & 4.5 s / GPU & \\
Re-3DOP & & 67.24 \% & 81.51 \% & 64.02 \% & 3 s / 1 core & \\
Mono3D & & 66.66 \% & 77.30 \% & 63.44 \% & 4.2 s / GPU & X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.\\
IVA & & 66.50 \% & 75.89 \% & 61.60 \% & 1 s / GPU & \\
HM\_SSD\_RCNN & & 66.41 \% & 82.33 \% & 59.21 \% & 0.15 s / 1 core & \\
HSR2 & & 65.91 \% & 78.05 \% & 63.05 \% & 0.15 s / 1 core & \\
Faster R-CNN & & 65.91 \% & 78.35 \% & 61.19 \% & 2 s / GPU & S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.\\
Tx & & 65.08 \% & 77.36 \% & 59.43 \% & 2 s / GPU & \\
DJML & & 64.93 \% & 77.15 \% & 58.96 \% & 2.4 s / GPU & \\
PNET & & 64.66 \% & 75.71 \% & 60.41 \% & 0.1 s / GPU & \\
tbd & & 64.56 \% & 79.59 \% & 61.27 \% & 1 s / 1 core & \\
SDP+CRC (ft) & & 64.25 \% & 77.81 \% & 59.31 \% & 0.6 s / GPU & 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.\\
Pose-RCNN & & 63.38 \% & 77.69 \% & 57.42 \% & 2 s / >8 cores & \\
CFM & & 63.26 \% & 74.21 \% & 56.44 \% & & 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.\\
PCN & & 62.08 \% & 74.71 \% & 56.68 \% & 0.6 s / & \\
RPN+BF & & 61.29 \% & 75.58 \% & 56.08 \% & 0.6 s / GPU & L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.\\
Regionlets & & 61.16 \% & 72.96 \% & 55.22 \% & 1 s / >8 cores & 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.\\
RB & & 61.15 \% & 77.08 \% & 55.12 \% & 0.6 s / GPU & \\
LC & & 60.68 \% & 71.98 \% & 54.47 \% & 1 s / 1 core & \\
ens & & 60.64 \% & 72.30 \% & 54.59 \% & / & \\
CompACT-Deep & & 58.73 \% & 69.70 \% & 52.69 \% & 1 s / 1 core & Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.\\
FichaDet & & 58.72 \% & 70.16 \% & 53.01 \% & 0.2 s / 4 cores & \\
DeepParts & & 58.68 \% & 70.46 \% & 52.73 \% & ~1 s / GPU & Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.\\
FilteredICF & & 57.12 \% & 69.05 \% & 51.46 \% & ~ 2 s / >8 cores & S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.\\
p2dv & & 56.98 \% & 68.71 \% & 50.99 \% & 1 s / 1 core & \\
D-TSF & & 56.77 \% & 69.03 \% & 50.77 \% & 1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
FD2 & & 56.68 \% & 71.09 \% & 51.65 \% & 0.01 s / GPU & \\
MV-RGBD-RF & la & 56.59 \% & 73.05 \% & 49.63 \% & 4 s / 4 cores & 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.\\
ACNet+Cascad & & 56.23 \% & 66.17 \% & 50.67 \% & 2.5 s / 1 core & \\
Vote3Deep & la & 55.38 \% & 67.94 \% & 52.62 \% & 1.5 s / 4 cores & 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.\\
FD & & 55.33 \% & 67.87 \% & 50.02 \% & 0.01 s / GPU & \\
pAUCEnsT & & 54.58 \% & 66.11 \% & 48.49 \% & 60 s / 1 core & S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.\\
ANM & & 54.02 \% & 70.43 \% & 49.83 \% & 0.05 s / GPU & \\
PDV2 & & 53.74 \% & 65.71 \% & 49.47 \% & 3.7 s / 1 core & 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.\\
ANM & & 52.55 \% & 69.86 \% & 51.13 \% & 0.05 s / GPU & \\
ACFD & & 50.91 \% & 61.59 \% & 45.51 \% & 0.2 s / 4 cores & \\
ZGC & & 50.42 \% & 67.07 \% & 42.79 \% & 0.12 s / 1 core & \\
R-CNN & & 50.20 \% & 62.05 \% & 44.85 \% & 4 s / GPU & J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.\\
SSD1 & & 50.14 \% & 63.93 \% & 47.46 \% & 0.255 s / GPU & \\
NMF-CNN & & 49.26 \% & 65.16 \% & 45.38 \% & 0.1 s / GPU & \\
ACF & & 47.29 \% & 60.11 \% & 42.90 \% & 1 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.\\
Fusion-DPM & la & 46.67 \% & 59.38 \% & 42.05 \% & ~ 30 s / 1 core & C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.\\
ACF-MR & & 46.23 \% & 58.85 \% & 42.10 \% & 0.6 s / 1 core & R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.\\
HA-SSVM & & 45.51 \% & 58.91 \% & 41.08 \% & 21 s / 1 core & J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.\\
DPM-VOC+VP & & 44.86 \% & 59.60 \% & 40.37 \% & 8 s / 1 core & 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.\\
ACF-SC & & 44.77 \% & 54.20 \% & 39.57 \% & & 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.\\
SquaresICF & & 44.42 \% & 57.47 \% & 40.08 \% & 1 s / GPU & R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.\\
AR-FCN & & 43.88 \% & 53.16 \% & 35.58 \% & 0.19 s / GPU & \\
QHY & & 43.42 \% & 60.19 \% & 42.31 \% & 0.1 s / 1 core & \\
SubCat & & 42.34 \% & 54.06 \% & 37.95 \% & 1.2 s / 6 cores & E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.\\
HL & & 42.31 \% & 58.63 \% & 34.87 \% & 0.16 s / 1 core & \\
RCNN & & 42.17 \% & 58.48 \% & 34.88 \% & 0.08 s / GPU & \\
Fast-RCNN-SS & & 41.59 \% & 54.20 \% & 35.26 \% & 1 s / GPU & \\
ACF & & 40.62 \% & 49.08 \% & 36.66 \% & 0.2 s / 1 core & 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). .\\
NMRDO & & 40.59 \% & 55.43 \% & 39.75 \% & 0.1 s / GPU & \\
ACFK & & 40.23 \% & 48.83 \% & 33.57 \% & 0.07 s / 1 core & \\
ACF\_M & & 39.36 \% & 51.75 \% & 35.95 \% & 0.1 s / 1 core & \\
LSVM-MDPM-sv & & 39.36 \% & 51.75 \% & 35.95 \% & 10 s / 4 cores & 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.\\
PCNN & & 39.07 \% & 53.60 \% & 37.91 \% & 1 s / 1 core & \\
CNN & & 38.98 \% & 52.85 \% & 38.31 \% & 1 s / 1 core & \\
LSVM-MDPM-us & & 38.35 \% & 50.01 \% & 34.78 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
Vote3D & la & 35.74 \% & 44.47 \% & 33.72 \% & 0.5 s / 4 cores & D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.\\
mBoW & la & 31.37 \% & 44.36 \% & 30.62 \% & 10 s / 1 core & 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.\\
DPM-C8B1 & st & 29.03 \% & 38.96 \% & 25.61 \% & 15 s / 4 cores & 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.\\
YOLO & & 24.35 \% & 25.63 \% & 17.50 \% & 0.03 s / GPU & \\
R-CNN\_VGG & & 23.16 \% & 28.95 \% & 22.17 \% & 10 s / GPU & \\
YOLOv2 & & 16.19 \% & 20.80 \% & 15.43 \% & 0.02 s / GPU & 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.\\
BIP-HETERO & & 13.38 \% & 14.85 \% & 13.25 \% & ~2 s / 1 core & 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.
\end{tabular}