Survey on the latest development of pedestrian detection system and its key technologies expectation

Tien Tzu Hsueh Pao/Acta Electronica Sinica

Volumn 36, Issue 5, 2008, Pages 962-968

  Xu, Yan Wu ^a,b   Cao, Xian Bin ^a,b   Qiao, Hong ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b Anhui Province Key Laboratory of Software in Computing and Communication   (China)

^c INSTITUTE OF AUTOMATION   (China)

Author keywords

Classification mechanism; Feature selection; Multi sensor fusion; Pedestrian detection system

Indexed keywords

SENSORS; SIGNAL DETECTION; SURVEYS; TECHNOLOGY;

ACTIVE SAFETY; CLASSIFICATION MECHANISM; FEATURE SELECTION; FUTURE WORK; KEY TECHNOLOGIES; LATEST DEVELOPMENT; MULTI-SENSOR FUSION; PEDESTRIAN DETECTION SYSTEM; PEDESTRIAN DETECTION SYSTEMS; PROTOTYPE SYSTEMS; SENSOR TECHNOLOGIES;

PEDESTRIAN SAFETY;

EID: 45949100749     PISSN: 03722112     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (45)

1. Viola P, Jones M, Snow D. Detecting pedestrians using patterns of motion and appearance [A]. Proceedings of International Conference on Computer Vision [C]. Washington DC, USA: IEEE, 2003. 734-741.
2. Zhao L, Thorpe C. Stereo and neural network-based pedestrian detection [J]. IEEE Transactions on Intelligent Transportation Systems, 2000, 1 (3): 148-154.
3. Oren M, Papageorgiou C, Sinha P, Osuna E, Poggio T. Pedestrian detection using wavelet templates [A]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition [C]. San Juan, Puerto Rico: IEEE, 1997. 193-199.
4. Mohan A, Papageorgiou C, Poggio T. Example-based object detection in images by components [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23 (4): 349-361.
5. Xu F L, Liu X, Fujimura K. Pedestrian detection and tracking with night vision [J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6 (1): 63-71.
6. Gavrila D M, Giebel J, Munder S. Vision-based pedestrian detection: The PROTECTOR system [A]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium [C]. Parma, Italy: IEEE, 2004. 13-18.
7. Cheng H, Zheng N N, Qin J J. Pedestrian detection using sparse Gabor filter and support vector machine [A]. Proceedings of IEEE Intelligent Vehicles Symposium [C]. Las Vegas, USA: IEEE, 2005. 583-587.
8. Tian Q M, Sun H, Luo Y P, Hu D C. Nighttime pedestrian detection with a normal camera using SVM classifier [A]. Proceedings of International Symposium on Neural Networks [C]. Chongqing, China: Springer, 2005. 3497. 189-194.
9. 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)
10. GUO Lie, WANG Rong-ben, GU Bai-yuan, YU Tian-hong. Overview of worldwide intelligent vehicle pedestrian detection technology [J]. Journal of Highway and Transportation Research and Development, 2005, 22 (11): 133-137. (in Chinese)
11. Guo L, Wang R B, Jin L S, Li L H, Yang L. Algorithm study for pedestrian detection based on monocular vision [A]. Proceedings of IEEE International Conference on Vehicular Electronics and Safety [C]. Shanghai, China: IEEE, 2006. 83-87.
12. Cao X B, Qiao H, Wang F Y, Zhang X Z. Application of cooperative co-evolution in pedestrian detection systems [A]. Proceedings of IEEE International Conference on Intelligence and Security Informatics [C]. Atlanta, USA: Springer, 2005. 3495. 664-665.
13. Xu Y W, Cao X B, Qiao H. Optical camera based pedestrian detection in rainy or snowy weather [A]. Proceedings of IEEE International Conference on Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 06) [C]. Xi'an, China: Springer, 2006. 4223. 1182-1191.
14. Chen D, Cao X B, Xu Y W, Qiao H, Wang F Y. A SVM-based classifier with shape and motion features for pedestrian detection system [A]. Proceedings of IEEE Intelligent Vehicle Symposium [C]. Meguroku, Japan: IEEE, 2006. 331-335.
15. Guo Y P, Cao X B, Xu Y W, Qiao H. Co-evolution based feature selection for pedestrian detection [A]. Proceedings of IEEE International Conference on Control and Automation [C]. Guangzhou, China, 2007. 2797-2801.
16. Cao X B, Qiao H, Keane J. A low-cost pedestrian detection system with a single optical camera [J]. IEEE Transactions on Intelligent Transportation System, 2008, 9 (1): 58-67.
17. Wei C X, Cao X B, Y W Xu, Qiao H, Wang F Y. The treelike assembly classifier for pedestrian detection [A]. Proceedings of Pacific Asian Workshop on Intelligence and Security Informatics [C]. Chengdu, China: Springer, 2007. 232-237.
18. Gandhi T, Trivedi M M. Pedestrian collision avoidance systems: A survey of computer vision based recent studies [A]. Proceeding of Intelligent Transportation Systems Conference [C]. Toronto, Canada: IEEE, 2006. 976-981.
19. Gavrila D M. Pedestrian detection from a moving vehicle [A]. Proceedings of European conference on Computer Vision [C]. London, UK: Springer, 2000. 1843. 37-49.
20. Lipton A. Local application of optic flow to analyze rigid versus non-rigid motion [A]. Proceedings of International Conference on Computer Vision Workshop on Frame-Rate Vision [C]. Corfu, Greece: IEEE, 1999, http://www.ri.cmu.edu/pubs/pub-2934.html.
21. Heisele B, Wöhler C. Motion-based recognition of pedestrians [A]. Proceedings of IEEE International Conference on Pattern Recognition [C]. Brisbane, Australia: IEEE, 1998. 2. 1325-1330.
22. Sessler G M A, Martoyo T, Jondral F K. RBF based multiuser detectors for UTRA-TDD [A]. Proceedings of IEEE Vehicular Technology Conference [C]. Atlantic, USA: IEEE, 2001. 1. 484-486.
23. Franke U, Gavrila D M, Gorzig S, Lindner F, Puetzold F, Wöhler C. Autonomous driving goes downtown [J]. IEEE Intelligent Systems and Their Applications, 1998, 13 (6): 40-48.
24. Gavrila D M, Geibel J. Shape-based pedestrian detection and tracking [A]. Proceedings of IEEE Intelligent Vehicles Symposium [C]. Versailles, France: IEEE, 2003. 1. 8-14.
25. Papageorgiou C, Poggio T. Trainable pedestrian detection system [A]. Proceeding of IEEE International Conference on Image Processing [C]. Kobe, Japan: IEEE, 1999. 4. 35-39.
26. Ran Y, Zheng Q F, Weiss I, Davis L S, Abd-Almageed W, Zhao L. Pedestrian classification from moving platforms using cyclic motion pattern [A]. Proceedings of International Conference on Image Processing [C]. Genova, Italy: IEEE, 2005. 2173-2176.
27. Shashua A, Gdalyahu Y, Hayun G. Pedestrian detection for driving assistance systems-single-frame classification and system level performance [A]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium [C]. Parma, Italy: IEEE, 2004. 1-6.
28. Havasi L, Szlávik Z, Sziranyi T. Pedestrian detection using derived third-order symmetry of legs [J]. Computational Imaging and Vision, 2006, 32: 733-739.
29. Lowe D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60 (2): 91-110.
30. Elzein H, Lakshmanan S, Watta P. A motion and shape-based pedestrian detection algorithm [A]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium [C]. Columbus, USA: IEEE, 2003. 500-504.
31. Fardi B, Schuenert U, Wanielik G. Shape and motion-based pedestrian detection in infrared images: A multi sensor approach [A]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium [C]. Las Vegas, USA: IEEE, 2005. 18-23.
32. Dai C X, Zheng Y F, Li X. Pedestrian detection and tracking in infrared imagery using shape and appearance [J]. Computer Vision and Image Understanding, 2007, 106 (2-3): 288-299.
33. Bertozzi M, Broggi A, Lasagni A, Rose M D. Infrared stereo vision-based pedestrian detection [A]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium [C]. Las Vegas, USA: IEEE, 2005. 24-29.
34. Munder S, Gavrila D M. An experimental study on pedestrian classification [J]. Transactions on Pattern Analysis and Machine Intelligence, 2006, 28 (11): 1863-1868.
35. Grubb G, Zelinsky A, Nilsson L, Rilbe M. 3D vision sensing for improved pedestrian safety [A]. Proceedings of IEEE International Conference on Intelligent Vehicle Symposium [C]. Parma, Italy: IEEE, 2004. 19-24.
36. Schauland S, Kummert A. Implementation and optimization of wavelet and symmetry features for vision-based pedestrian detection [A]. Proceedings of International Conference on Graphics and Visualization in Engineering [C]. Clearwater, USA: ACTA Press, 2007. 19-24.
37. Abramson Y, Steux B. Hardware-friendly pedestrian detection and impact prediction [A]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium [C]. Parma, Italy: IEEE, 2004. 590-595.
38. Fuerstenberg K C, Dietmayer K C J, Willhoeft V. Pedestrian recognition in urban traffic using a vehicle based multilayer Laserscanner [A]. Proceedings of IEEE Intelligent Vehicles Symposium [C]. Versailles, France: IEEE, 2002. 1. 31-35.
39. Kato T, Ninomiya Y, Masaki I. An obstacle detection method by fusion of radar and motion stereo [J]. IEEE Transactions on Intelligent Transportation Systems, 2002, 3 (3): 182-188.
40. Jung H G, Lee Y H, Yoon P J, Hwang I Y, Kim J. Sensor fusion based obstacle detection/classification for active pedestrian protection system [A]. Proceedings of Advances in Visual Computing [C]. Lake Tahoe, USA: Springer, 2006. 4292. 294-305.
41. Zhao J, Yao D Y. Occlusion adaptive object tracking based on video image and radar data [A]. Proceedings of IEEE International Conference on ITS Telecommunications [C]. Chengdu, China: IEEE, 2006. 943-946.
42. Torresan H, Turgeon B, Ibarra-Castanedo C, Hebert P, Maldague X. Advanced surveillance systems: Combining video and thermal imagery for pedestrian detection [A]. Proceedings of the Society of Photo-optical Instrumentation Engineers [C]. Orlando, USA: SPIE-INT Society Optical Engineering, 2004. 5405. 506-515.
43. Huang Y P, Fu S, Thompson C. Stereovision-based object segmentation for automotive applications [J]. Journal on Applied Signal Processing, 2005, 14: 2322-2329.
44. Sheng W H, Yang Q Y, Guo Y. Cooperative driving based on inter-vehicle communications: Experimental platform and algorithm [A]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots Systems [C]. Beijing, China: IEEE, 2006. 5073-5078.
45. Mangharam R, Weller D, Rajkumar R, Mudalige P, Ba F. GrooveNet: A hybrid simulator for vehicle-to-vehicle networks [A]. Proceedings of Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services [C]. San Jose, USA: IEEE, 2006. 414-421.