A survey on pedestrian detection

Tien Tzu Hsueh Pao/Acta Electronica Sinica

Volumn 40, Issue 4, 2012, Pages 814-820

  Su, Song Zhi ^a   Li, Shao Zi ^a   Chen, Shu Yuan ^b   Cai, Guo Rong ^a,c   Wu, Yun Dong ^c  

^a XIAMEN UNIVERSITY   (China)

^b YUAN ZE UNIVERSITY   (Taiwan)

^c JIMEI UNIVERSITY   (China)

Author keywords

Driver assistance systems; Intelligent surveillance; Object detection; Pedestrian detection

Indexed keywords

ACTIVE AREA; DRIVER ASSISTANCE SYSTEM; HYBRID FEATURES; INTELLIGENT SURVEILLANCE; LOW-LEVEL FEATURES; OBJECT DETECTION; PEDESTRIAN DETECTION; RESEARCH TRENDS; SLIDING WINDOW;

COMPUTER VISION; FEATURE EXTRACTION; SURVEYS;

DETECTORS;

EID: 84862697935     PISSN: 03722112     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.0372-2112.2012.04.031     Document Type: Review

References (55)

1. Xu Yan-wu, Cao Xian-bin, and Qiao Hong. Survey on the latest development of pedestrian detection system and its key technologies expectation [J]. Acta Electronica Sinica, 2008, 36(5): 368-376.(in Chinese)
2. Jia Hui-xing, Zhang Yu-jin. A survey of computer vision based pedestrian detection for driver assistance systems [J]. Acta Automatica Sinica, 2007, 33(1): 84-90.(in Chinese)
3. Geronimo D, Lopez A, Sappa A, et al. Survey of pedestrian detection for advanced driver assistance systems [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2010, 32(7): 1239-1258.
4. Dollár P, Wojek C, Schiele B, et al. Pedestrian detection: an evaluation of the state of the art. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2011, 99: 1-20.
5. Aggarwal J, Ryoo M. Human activity analysis: a review [J]. ACM Computing Surveys, 2011, 43(3), 16: 1-47.
6. Du You-tian, Chen Feng, Xu Wen-li, et al. A survey on the vision-based human motion recognition [J]. Acta Electronica Sinica, 2007. 35(1): 84-90.(in Chinese)
7. Reilly V, Solmaz B, and Shah M. Geometric constraints for human detection in aerial imagery [A]. In Proc. ECCV [C], 2010.
8. Andriluka M, Schnitzspan P, Meyer J, et al. Vision based victim detection from unmanned aerial vehicles [A]. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) [C]. Taipei, Taiwan, 2010.
9. Dollar P, Belongie S, Perona P. The fastest pedestrian detector in the west [A]. In Proc. BMVC [C], 2010.
10. Enzweiler M, Gavrila D. Monocular pedestrian detection: survey and experiments [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2179-2195.
11. Dalal N, Triggs B. Histograms of oriented gradients for human detection [A]. In Proc. IEEE CVPR [C], 2005, 886-893.
12. Zhu Q, Yeh C, Cheng T. Fast human detection using a cascade of histograms of oriented gradients [A]. In Proc. IEEE CVPR [C], 2006, 2: 1491-1498.
13. Wojek C, B. Schiele. A performance evaluation of single and multi-feature people detection [A]. In Proc. DAGM [C], 2008.
14. Ojala T, Pietikainen M, Harwood D. A comparative study of texture measures with classification based on feature distributions [J]. Pattern Recognition, 1996, 19(3): 51-59.
15. Mu Y, Yan S, Liu Y, et al. Discriminative local binary patterns for pedestrian detection in personal album [A]. In Proc. IEEE CVPR [C], 2008.
16. Wang X, Han X, Yan S. A HOG-LBP human detector with partial occlusion handling [A]. In Proc. IEEE ICCV [C], 2009.
17. Walk S, Majer N, Schindler K, et al. New features and insights for pedestrian detection [A]. In Proc. IEEE CVPR [C], 2010.
18. Wu J X, Rehg J M. CENTRIST: A visual descriptor for scene categorization [J]. IEEE Trans. on Pattern Analysis and Machine Intelligent, 2011, 33(8): 1489-1501.
19. Wu J X, Geyer C, Rehg J M. Real-time human detection using contour cues [A]. In Proc. IEEE ICRA, 2011. 860-867.
20. Walk S, Majer N, Schindler K, et al. New features and insights for pedestrian detect ion [A]. In Proc. of CVPR [C], 2010.
21. Viola P, Jones M. Robust real-time face detection [J]. International Journal of Computer Vision, 2004, 57(2): 137-154.
22. Viola P, Jones M, Snow D. Detecting pedestrians using patterns of motion and appearance [J], International Journal of Computer Vision, 2005, 63(2): 153-161.
23. Wu B, Nevatia R. Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors [A]. In Proc. IEEE ICCV [C], 2005.
24. Gavrila D. A bayesian, exemplar-based approach to hierarchical shape matching [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(8): 1-14.
25. Gao W, Ai H, Lao S. Adaptive contour features in oriented granular space for human detection and segmentation [A]. In Proc. IEEE CVPR [C], 2009.
26. Tuzel O, Porikli F, Meer P. Pedestrian detection via classification on Riemannian manifolds [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(10): 1713-1727.
27. Wantanbe T, Ito S, Yokoi K. Co-occurrence histograms of oriented gradients for pedestrian detection [A]. In Proc. of The 3rd Pacific-Rim Symposium on Image and Video Technology [C], Tokyo, Japan, 2009. 37-47.
28. Liu Y, Shan S, Zhang W, et al. Granularity-tunable gradients partition (GGP) descriptors for Human Detection [A]. In Proc. IEEE CVPR [C], 2009.
29. Dollar P, Tu Z W, Perona P, et al. Integral channel features [A]. In Proc. BMVC [C], 2009, 1-11.
30. Schwartz W, Kembhavi A, Harwood D, et al. Human detection using partial least squares analysis [A]. In Proc. IEEE ICCV [C], 2009.
31. Wojek C, Walk S, Schiele B, Multi-cue onboard pedestrian detection [A]. In Proc. IEEE CVPR [C], 2009.
32. Li Fei-fei, Perona P. A bayesian hierarchal model for learning natural scene categories [A]. In Proc. IEEE CVPR [C], 2005.
33. Leibe B, Leonardis A, Schiele B. Robust object detection with interleaved categorization and segmentation [J]. International Journal of Computer Vision, 2008, 77(3): 259-289.
34. Lampert C, Blaschko M, Hofmann T.Efficient subwindow search: a branch and bound framework for object localization [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2129-2142.
35. Chum O, Zisserman A. An exemplar model for learning object classes [A]. In Proc. IEEE CVPR [C], 2007.
36. Su Song-zhi. Research on key technologies of pedestrian detection [D]. Fujian, Xiamen, Xiamen University, Ph. D Thesis, 2011.(in Chinese)
37. Tian Guang, Qi Fei-hu. Feature transformation and SVM based hierarchical pedestrian detection with a monocular moving camera [J]. Acta Electronica Sinica, 2008, 36(5): 1024-1028.(in Chinese)
38. Oren M, Papageorion C, Sinha P, et al. Pedestrian detection using wavelet templates [A]. In Proc. IEEE CVPR [C]. 1997. 193-199.
39. Maji S, Berg A, Malik J. Classification using intersection kernel SVMs is efficient [A]. In Proc. IEEE CVPR [C], 2008.
40. Chen Y T, Chen C S. Fast human detection using a novel boosted cascading structure with meta stages [J]. IEEE Trans. on Image Processing, 2008, 17(8): 1452-1464.
41. Kim T K, Cipolla R. MCBoost: Multiple classifiers boosting for perceptual co-clustering of images and visual features [A]. In Proc. IEEE NIPS [C], 2008.
42. Lin Z, Hua G, Davis S. Multiple instance feature for robust part-based object detection [A]. In Proc. IEEE CVPR [C], 2009.
43. Babenko B, Dollar P, Tu Z, et al. Simultaneous learning and alignment: multi-instance and multi-pose learning [A]. In Proc. IEEE ECCV [C], 2008.
44. Felzenszwalb P, Girshick R, McAllester D, et al. Object detection with discriminatively trained part based models [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009, 32(9): 1627-1645.
45. Felzenszwalb P, Girshick R, McAllester D. Cascade object detection with deformable part models [A]. In Proc. IEEE CVPR [C], 2010.
46. Pedersoli M, Vedaldi A, Gonzalez J. A coarse-to-fine approach for fast deformable object detection [A]. In Proc. IEEE CVPR [C], 2011.
47. Parikh D, Zitnick C. Finding the weakest link in person detectors [A]. In Proc. IEEE CVPR [C], 2011.
48. Agarwal A, Awan A, Roth D. Learning to detect objects in images via a sparse, part-based representation [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2004, 26(11): 1475-1490.
49. An Senjian, Peursum P, Liu Wanquan, et al. Efficient algorithms for subwindow search in object detection and localization [A]. In Proc. IEEE CVPR [C], 2009.
50. An Senjian, Peursum P, Liu Wanquan, et al. Exploiting monge structures in optimum subwindow search [A]. In Proc. IEEE CVPR [C], 2010.
51. Vijayanarasimhan S, Grauman K. Efficient region search for object detection [A]. In Proc. IEEE CVPR [C], 2011.
52. Yuan J S, Liu Z C, Wu Y. Discriminative video pattern search for efficient action detection [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2011, 99: 1-17.
53. Gall J, Lempitsky V. Class-specific hough forests for object detection [A]. In Proc. IEEE CVPR [C], 2009.
54. Maji S, Malik J. Object detection using a max-margin hough transform [A]. In Proc. IEEE CVPR [C], 2009.
55. Shashua A, Gdalyahu Y, Hayun G. Pedestrian detection for driving assistance systems: single-frame classification and system level performance [A]. In Proc. IEEE Intelligent Vehicles Symposium [C], 2004. 1-6.