What Can Help Pedestrian Detection?
Open accessAbstract
Aggregating extra features has been considered as an effective approach to boost traditional pedestrian detection methods. However, there is still a lack of studies on whether and how CNN-based pedestrian detectors can benefit from these extra features. The first contribution of this paper is exploring this issue by aggregating extra features into CNN-based pedestrian detection framework. Through extensive experiments, we evaluate the effects of different kinds of extra features quantitatively. Moreover, we propose a novel network architecture, namely HyperLearner, to jointly learn pedestrian detection as well as the given extra feature. By multi-task training, HyperLearner is able to utilize the information of given features and improve detection performance without extra inputs in inference. The experimental results on multiple pedestrian benchmarks validate the effectiveness of the proposed HyperLearner.
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Many recent pedestrian detection methods are extensions of Fast/Faster R-CNN and profit from the basic concepts of these methods.
“Many recent pedestrian detection methods [62], [63], [71], [55], [70], [57], [56] are extensions of Fast/Faster R-CNN and profit from the basic concepts of these methods.”
From EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes (2019) · cites this paper as [71]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
(2016) · Cited by 31,590 · see citing works
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint
Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135 · see citing works
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213 · see citing works
Holistically-Nested Edge Detection
Xie, S., & Tu, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.164 · Cited by 3,401 · see citing works
Fast Feature Pyramids for Object Detection
Dollár, P., Appel, R., Belongie, S., & Perona, P. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2300479 · Cited by 1,941 · see citing works
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Cai, Z., Fan, Q., Feris, R. S., & Vasconcelos, N. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_22 · Cited by 1,605 · see citing works
Pedestrian detection: A benchmark
Dollár, P., Wojek, C., Schiele, B., & Perona, P. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206631 · Cited by 1,335 · see citing works
Evaluation of local spatio-temporal features for action recognition
Wang, H., Ullah, M. M., Kläser, A., Laptev, I., & Schmid, C. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.124 · Cited by 1,295 · see citing works
The computation of optical flow
Beauchemin, S. S., & Barron, J. A. (1995) · ACM Computing Surveys · doi:10.1145/212094.212141 · Cited by 1,282 · see citing works
Dollar, P., Tu, Z., Perona, P., & Belongie, S. (2009) · Procedings of the British Machine Vision Conference 2009 · doi:10.5244/c.23.91 · Cited by 1,237 · see citing works
Mayer, N., Ilg, E., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · Cited by 1,214 · see citing works
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Kong, T., Yao, A., Chen, Y., & Sun, F. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.98 · Cited by 934 · see citing works
Is Faster R-CNN Doing Well for Pedestrian Detection?
Zhang, L., Lin, L., Liang, X., & He, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_28 · Cited by 852 · see citing works
(2015) · Cited by 656 · see citing works
How Far are We from Solving Pedestrian Detection?
Zhang, S., Benenson, R., Omran, M., Hosang, J., & Schiele, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.141 · Cited by 473 · see citing works
New features and insights for pedestrian detection
Walk, S., Majer, N., Schindler, K., & Schiele, B. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540102 · Cited by 468 · see citing works
Filtered channel features for pedestrian detection
Zhang, S., Benenson, R., & Schiele, B. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298784 · Cited by 395 · see citing works
Convolutional Channel Features
Yang, B., Yan, J., Lei, Z., & Li, S. Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.18 · Cited by 310 · see citing works
Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection
Yu, X., Choi, W., Lin, Y., & Savarese, S. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.108 · Cited by 303 · see citing works
Exploring Weak Stabilization for Motion Feature Extraction
Park, D., Zitnick, C. L., Ramanan, D., & Dollár, P. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.371 · Cited by 125 · see citing works
Scale-aware Fast R-CNN for Pedestrian DetectionPreprint
Li, J., Liang, X., Shen, S., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.08160 · Cited by 114 · see citing works
Semantic Channels for Fast Pedestrian Detection
Costea, A. D., & Nedevschi, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.259 · Cited by 66 · see citing works
Local Decorrelation For Improved DetectionPreprint
Nam, W., Dollár, P., & Han, J. H. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.1134 · Cited by 52 · see citing works
Flagged first, then by citations
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Sorted by citations
Recent advances in deep learning for object detection
Wu, X., Sahoo, D., & Hoi, S. C. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.085 · Cited by 1,047 · see citing works
A review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584 · see citing works
CrowdHuman: A Benchmark for Detecting Human in a Crowd
Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., & Sun, J. (2018) · arXiv · doi:10.48550/arxiv.1805.00123 · Cited by 534 · see citing works
Self-citationOcclusion-Aware R-CNN: Detecting Pedestrians in a Crowd
Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_39 · Cited by 488 · see citing works
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Liu, S., Huang, D., & Wang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00662 · Cited by 413 · see citing works
MegDet: A Large Mini-Batch Object Detector
Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., Yu, G., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00647 · Cited by 311 · see citing works
Self-citationEuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes
Braun, M., Krebs, S., Flohr, F., & Gavrila, D. M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2897684 · Cited by 307 · see citing works
A Review on Object Detection Based on Deep Convolutional Neural Networks for Autonomous Driving
Lu, J., Tang, S., Wang, J., Zhu, H., & Wang, Y. (2019) · 2019 Chinese Control And Decision Conference (CCDC) · doi:10.1109/ccdc.2019.8832398 · Cited by 17 · see citing works
- Authors
- Mao, J., Xiao, T., Jiang, Y., & Cao, Z.
- Affiliations
- Tsinghua University (CN)
- Peking University (CN)
- Megvii (China) (CN)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 6034-6043
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2613599172
- Cited by
- 306
- References
- 36
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 18 · 2023: 23 · 2022: 37 · 2021: 52 · 2020: 57 · 2019: 68 · 2018: 33
- FWCI
- 12.63
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- pedestrian detection, multi-task learning, representation learning, network architecture
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