Pedestrian detection aided by deep learning semantic tasks

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 5079-5087

  Tian, Yonglong ^a   Luo, Ping ^a,b   Wang, Xiaogang ^a,b   Tang, Xiaoou ^a,b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; OBJECT DETECTION; SEMANTICS;

BINARY CLASSIFICATION; CONVERGENCE RATES; DIFFERENT DISTRIBUTIONS; DISCRIMINATIVE FEATURES; HIGH-LEVEL FEATURES; MULTIPLE DATA SOURCES; PEDESTRIAN DETECTION; SCENE SEGMENTATION;

PATTERN RECOGNITION;

EID: 84959241019     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299143     Document Type: Conference Paper

References (37)

1. R. Benenson, M. Mathias, R. Timofte, and L. Van Gool. Pedestrian detection at 100 frames per second. In CVPR, pages 2903-2910, 2012.
2. R. Benenson, M. Omran, J. Hosang, and B. Schiele. Ten years of pedestrian detection, what have we learned? In ECCV Workshop, 2014.
3. G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla. Segmentation and recognition using structure from motion point clouds. In ECCV, pages 44-57, 2008.
4. G. Chen, Y. Ding, J. Xiao, and T. X. Han. Detection evolution with multi-order contextual co-occurrence. In CVPR, pages 1798-1805, 2013.
5. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR. 2005.
6. Y. Deng, P. Luo, C. C. Loy, and X. Tang. Pedestrian attribute recognition at far distance. In ACM Multimedia, 2014.
7. P. Dollár, R. Appel, S. Belongie, and P. Perona. Fast feature pyramids for object detection. TPAMI, 2014.
8. P. Dollár, Z. Tu, P. Perona, and S. Belongie. Integral channel features. In BMVC, 2009.
9. P. Dollár, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: An evaluation of the state of the art. TPAMI, 34, 2012.
10. A. Ess, B. Leibe, and L. Van Gool. Depth and appearance for mobile scene analysis. In ICCV, pages 1-8, 2007.
11. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. TPAMI, 32(9):1627-1645, 2010.
12. S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In ICCV, pages 1-8, 2009.
13. J. Hosang, M. Omran, R. Benenson, and B. Schiele. Taking a deeper look at pedestrians. In CVPR, 2015.
14. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097-1105, 2012.
15. Z. Lin and L. S. Davis. Shape-based human detection and segmentation via hierarchical part-template matching. TPAMI, 32(4):604-618, 2010.
16. P. Luo, Y. Tian, X. Wang, and X. Tang. Switchable deep network for pedestrian detection. In CVPR, pages 899-906, 2014.
17. P. Luo, X. Wang, and X. Tang. Pedestrian parsing via deep decompositional network. In ICCV, 2013.
18. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, pages 807-814, 2010.
19. W. Nam, P. Dollár, and J. H. Han. Local decorrelation for improved pedestrian detection.
20. T. Nortcliffe. People analysis cctv investigator handbook. In Home Office Centre of Applied Science and Technology, 2011.
21. W. Ouyang and X. Wang. A discriminative deep model for pedestrian detection with occlusion handling. In CVPR, 2012.
22. W. Ouyang and X. Wang. Joint deep learning for pedestrian detection. In ICCV, 2013.
23. W. Ouyang, X. Zeng, and X. Wang. Modeling mutual visibility relationship in pedestrian detection. In CVPR, 2013.
24. S. Paisitkriangkrai, C. Shen, and A. v. d. Hengel. Pedestrian detection with spatially pooled features and structured ensemble learning. arXiv, 2014.
25. S. Paisitkriangkrai, C. Shen, and A. van den Hengel. Strengthening the effectiveness of pedestrian detection with spatially pooled features. In ECCV, pages 546-561. 2014.
26. D. Park, C. L. Zitnick, D. Ramanan, and P. Dollár. Exploring weak stabilization for motion feature extraction. In CVPR, pages 2882-2889, 2013.
27. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Nature, 323:533-536, 1986.
28. P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun. Pedestrian detection with unsupervised multi-stage feature learning. In CVPR. 2013.
29. J. Tighe and S. Lazebnik. Superparsing: scalable nonparametric image parsing with superpixels. In ECCV, pages 352-365. 2010.
30. P. Viola and M. J. Jones. Robust real-time face detection. IJCV, 57(2):137-154, 2004.
31. P. Viola, M. J. Jones, and D. Snow. Detecting pedestrians using patterns of motion and appearance. IJCV, 63(2):153-161, 2005.
32. X. Wang, T. X. Han, and S. Yan. An HOG-LBP human detector with partial occlusion handling. In ICCV, 2009.
33. J. Yan, X. Zhang, Z. Lei, S. Liao, and S. Z. Li. Robust multiresolution pedestrian detection in traffic scenes. In CVPR, pages 3033-3040, 2013.
34. X. Zeng, W. Ouyang, and X. Wang. Multi-stage contextual deep learning for pedestrian detection. In ICCV, pages 121-128, 2013.
35. S. Zhang, C. Bauckhage, and A. Cremers. Informed haarlike features improve pedestrian detection. In CVPR, pages 947-954, 2013.
36. S. Zhang, R. Benenson, and B. Schiele. Filtered channel features for pedestrian detection. In CVPR, 2015.
37. L. Zhu, Y. Chen, and A. Yuille. Learning a hierarchical deformable template for rapid deformable object parsing. TPAMI, 32(6):1029-1043, 2010.