Review on vehicle detection based on video for traffic surveillance

Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2008

Volumn , Issue , 2008, Pages 2961-2966

  Wang, Guolin ^a,b   Xiao, Deyun ^a   Gu, Jason ^b  

^a TSINGHUA UNIVERSITY   (China)

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

Shadow removes; Tracking; Vehicle detection

Indexed keywords

CRITICAL STEPS; FIXED TIME INTERVALS; REAL-TIME TRAFFICS; REGION OF INTERESTS; SHADOW REMOVES; STATIONARY CAMERAS; SURVEILLANCE SYSTEMS; THREE STEPS; TRACKING; TRAFFIC CONDITIONS; TRAFFIC FLOW DENSITIES; TRAFFIC PARAMETERS; TRAFFIC SPEEDS; TRAFFIC STATES; TRAFFIC SURVEILLANCES; VEHICLE DETECTION; VEHICLE DETECTIONS; VEHICLE TRACKING;

IMAGE SEGMENTATION; REAL TIME SYSTEMS; SECURITY SYSTEMS; TRAFFIC SURVEYS;

VEHICLES;

EID: 56449093920     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAL.2008.4636684     Document Type: Conference Paper

References (61)

1. Micchalopoulos P G, Vehicle Detection Through Video Image Processing AUTOSCOPE System [J]. IEEE Transaction on Vehicular Technology, 1991,40 (1):21-29
2. E. Dickmanns, "The Development of Machine Vision For Road Vehicles in the Last Decade," Proc. IEEE Intelligent Vehicle Symp., 2002.
3. M. Bertozzi, A. Broggi, and A. Fascioli, "Vision-Based intelligent Vehicles: State of the Art and Perspectives," Robotics and Autonomous Systems, vol. 32, pp. 1-16, 2000.
4. Anderson, C.A., Michalopoulos, P.G.; Jacobson, R.D.; Cost benefit analysis of video-based vehicle detection Vehicle Navigation and Information Systems Conference, 1995. Proceedings.30 July-2 Aug. 1995 Page(s):508 - 515
5. Meng Cao; Anh Vu; Barth, M.; A Novel Omni-Directional Vision Sensing Technique for Traffic Surveillance, IEEE Intelligent Transportation Systems Conference, 2007. Page(s):678 - 683
6. M. Bertozzi, A. Broggi, M. Cellario, A. Fascioli, P. Lombardi, and M. Porta, "Artifical Vision in Road Vehicles," Proc. IEEE, vol. 90, no. 7, pp. 1258-1271, 2002.
7. R. Bishop, "Intelligent Vehicle Applications Worldwide," IEEE Intelligent Systems, 2000.
8. D. Gavrila and U. Franke, and C. Wohler, and S. Gorzig, "Real-Time Vision for Intelligent Vehicles," IEEE Instrumentation and Measurement Magazine, pp. 22-27, 2001.
9. IEEE Trans. Intelligent Transportation Systems, special issue on Vision Applications and Technology for Intelligent Vehicles: Part I-Infrastructure, vol. 1, 2000.
10. IEEE Trans. Intelligent Transportation Systems, special issue on Vision Applications and Technology for Intelligent Vehicles: Part II-Vehicles, vol. 1, 2000.
11. N. Diehl, Object-Oriented Motion Estimation and Segmentation in Image Sequences, IEEE Trans. Image Processing, vol. 3, pp. 1,901-1,904, Feb. 1990.
12. Nikos Paragios, Rachid Deriche Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22 No.3 pp 266-280, March, 2000.
13. Lei Xie; Guangxi Zhu; Yuqi Wang; Haixiang Xu; Zhenming Zhang; Robust vehicles extraction in a video-based intelligent transportation systems IEEE 2005 International Conference on Communications, Circuits and Systems, Volume 2, 27-30 May 2005 Page(s) 887-890
14. H.H. Nagel, G. Socher, H. Kollnig, and M. Otte, Motion Boundary Detection in Image Sequences by Local Stochastic Tests, Proc. European Conf. Computer Vision, vol. II, pp. 305-315, 1994.
15. T. Aach and A. Kaup, Bayesian Algorithms for Adaptive Change Detection in Image Sequences Using Markov Random Fields, Signal Processing: Image Comm, vol. 7, pp. 147-160, 1995.
16. J.M. Odobez and P. Bouthemy, Robust Multiresolution Estimation of Parametric Motion Models, J. Visual Comm. and Image Representation, vol. 6, pp. 348-365, 1995.
17. N. Paragios and G. Tziritas, Adaptive Detection and Localization of Moving Objects in Image Sequences, Signal Processing: Image Comm., vol. 14, pp. 277-296, 1999.
18. Kalman K. and Brandt A., Moving Object Recognition Using an Adaptive Background, In Proc. Time-varying Image Processing and Moving Object Recognition, Amsterdam, Netherlands, pp 289-296, 1990
19. Stauffer C. and Grimson W., Adaptive Background Mixture Models for Real-time Tracking. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, 246-252, 1999
20. Baisheng Chen, Yunqi, Lei, and Wangwei Li, A Novel Background Model for Real-time Vehicle Detection. IEEE ICSP'04, 1276-1279,2004
21. Zhang Jinglei; Liu Zhengguang; A Vision-Based Road Surveillance System Using Improved Background Subtraction and Region Growing Approach IEEE Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing Volume 3, July 30 2007-Aug. 1 2007 Page(s):819 - 822
22. C. C. C. Pang,, W. W. L. Lam, and N. H. C. Yung. "A Novel Method for Resolving Vehicle Occlusion in a Monocular Traffic-image Sequence," IEEE Trans. on Intelligent Transportation Systems, vol. 5, no. 3, pp.129-141, Sep. 2004.
23. A. Mittal and N. Paragios. "Motion-Based Background Subtraction using Adaptive Kernel Density Estimation," CVPR,, Washington, DC, 302-309, 2004.
24. Junxian Wang, George Bebis and Ronald Miller, Overtaking Vehicle Detection Using Dynamic and Quasi-Static Background Modeling, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005,
25. Haritaogu I., Harwood D. and Davis L., W. Real-Time Surveillance of People and Their Activities, IEEE Transaction on Pattern Analysis and Machine Intelligence. 22, (8), 809-830, August, 2000
26. N. Friedman and S. Russell. "Image Segmentation in Vedio Sequences: A Probabilistic Approach,"Thirteenth Conf. on Uncertainty in Artificial Intelligence, pp. 175-181,1997.
27. Hanzi Wang; Suter, D.; A re-evaluation of mixture of Gaussian background modeling ICASSP '05). IEEE International Conference on video signal processing applications Vol. 2 Page(s) 1017 - 1020
28. Andrew H. S. Lai and Nelson H. C. Yung, "Vehicle-type Identification Through automated virtual loop assignment and blockbased direction-biased motion estimation", IEEE Transactions on Intelligent Transportation Systems, vol.1, no.2, pp. 86-97, Jun. 2000.
29. Dae-Woon Lim, Sung-Hoon Choi, Joon-Suk Jun, "Automated detection of all kinds of violations at a street intersection using real time individual vehicle tracking", IEEE International Conference on Image Analysis and Interpretation, pp. 126-129, Apr. 2002.
30. Y. Liu, P. Payeur, Vision-based Detection of Activity for Traffic Control, 2003 IEEE CCECE, 1347-1350, May 2003
31. H. Mori and N. Charkai, "Shadow and Rhythm as Sign Patterns of Obstacle Detection," Proc. Int'l Symp. Industrial Electronics, pp. 271-277, 1993.
32. C. Tzomakas and W. Seelen, "Vehicle Detection in Traffic Scenes Using Shadows," Technical Report 98-06, Institut für Neuroinformatik, Ruht-Universitat, Bochum, Germany, 1998.
33. Cucchiara R. et al., "Improving shadow suppression in moving object detection with HSV color information," IEEE Proc. International Conference on Intelligent Transportation Systems, pp.334-339, 2001.
34. Haixiang Xu, et.al. A Novel Algorithm of Moving Cast Shadow Suppression, ICSP 2006 Proceedings, Vol. (2) 16-20, 2006
35. HanKyu Moon, Rama Chellappa, and Azriel Ronsenfeld, Optimal Edge-Based Shape Detection, IEEE Transactions on Image Processing, Vol.11 No.(11), pp1209-1226, 2002.
36. Luo-Wei Tsai, Jun-Wei Hsieh and Kao-Chin Fan, Vehicle Detection Using Normalized Color and Edge Map, , IEEE Transactions on mage Processing, Vol.16 Issue(3), 850-864, March 2007.
37. M.Y. Siyal, A Neural Vision Based Approach For Intelligent Transportation System, IEEE ICIT'02, pp 456-460, 2002
38. T. Kalinke, C. Tzomakas, and W. von Seelen, "A Texture-Based Object Detection and an Adaptive Model-Based Classification," Proc. IEEE Int'l Conf. Intelligent Vehicles, pp. 143-148, 1998.
39. Z. Sun, G. Bebis, and R. Miller, "On-road vehicle detection using Gabor filters and support vector machines," IEEE International Conference on Digital Signal Processing, July 2002.
40. M. Bertozzi, A. Broggi, and S. Castelluccio, "A real-time oriented system for vehicle detection," Journal of Systems Architecture, pp. 317-325, 1997.
41. Jianguang Lou; Tieniu Tan; Weiming Hu; Hao Yang; Maybank, S.J.; 3-D model-based vehicle tracking, IEEE Transactions on Image Processing Volume 14, Issue 10, Oct. 2005 Page(s):1561 - 1569
42. G. Kogut and M. Trivedi, "Efficient and robust vehicle localization," presented at the 9th World Congr. Intelligent Transport Systems, 2002.
43. J. W. Lee, M. S. Kim, and I. S. Kweon, "A Kalman filter based visual tracking algorithm for an object moving in 3-D," in Proc. Int. Conf. Intelligent Robots and Systems, Sep. 1995, pp. 355-358.
44. M. S. Costa and L. G. Shapiro, "3-D object recognition and pose with relational indexing," Computer Vis. Image Understand., vol. 79, no. 3, pp. 64-407, 2000.
45. T. N. Tan and K. D. Baker, "Efficient image gradient based vehicle localization," IEEE Trans. Image Process., vol. 9, no. 11, pp. 1343-1356, Nov. 2000.
46. R. Cutler and L. S. Davis, "Model-based object tracking in monocular image sequences of road traffic scenes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 781-796, Aug. 2000.
47. Karsten Muller, et al. 3-D Reconstruction of a Dynamic Environment With a Fully Calibrated Background for Traffic Scenes, IEEE Trans. On Circuits and System for Video Technology, Vol.15, No.4, 538-549, April, 2005.
48. N. Ghosh and B.Bhanu, Incremental Vehicle 3-D Modeling from Video, IEEE ICPR'06, 2006
49. A. Yoneyama, C. H. Yeh, and C. -C. J. Kuo, "Moving Cast Shadow Elimination for Robust Vehicle Extraction based on 2D Joint Vehicle/Shadow Models", Proc. IEEE Conf. Advanced Video and Signal Based Surveillance, pp. 229-236, July 2003.
50. Iwasaki, Y.; Kurogi, Y.; Real-time robust vehicle detection through the same algorithm both day and night,. ICWAPR '07. 2007 IEEE International Conference on Wavelet Analysis and Pattern Recognition Volume 3, 2-4 Nov. 2007 Page(s):1008 - 1014
51. D. Meyer, J. Denzler, and H. Niemann, "Model based extraction of articulated objects in image sequences for gait analysis," in Proc. IEEE Int. Conf. Image Processing, 1998, pp. 78-81.
52. J. Barron, D. Fleet, and S. Beauchemin, "Performance of optical flow techniques," Int. J. Comput.Vis., vol. 12, no. 1, pp. 42-77, 1994.
53. R. Culter and L. Davis, "Robust real-time periodic motion detection, analysis and applications," IEEE Trans. Pattern Recognit. Machine Intell., vol. 13, pp. 129-155, Feb. 2000.
54. Yuan-Kai Wang; Shao-Hua Chen; A robust vehicle detection approach, 2005 IEEE Conference on Advanced Video and Signal Based Surveillance, 15-16 Sept. 2005 Page(s):117 - 122
55. Xiaoyuan Wang; Jinglei Zhang; A traffic incident detection method based on wavelet Mallat algorithm 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications 28-30 June 2005 Page(s):166 - 172
56. Wieslaw Pamula Advantages of Using a Space Filling Curve for Computing Wavelet Transforms of Road Traffic Images, IEEE ICIAP'03, 2003.
57. Ming Yin; Hao Zhang; Huadong Meng; Xiqin Wang; An HMMBased Algorithm for Vehicle Detection in Congested Traffic Situations 2007..IEEE Intelligent Transportation Systems Conference Sept. 30 2007-Oct. 3 2007 Page(s):736 - 741
58. Lei Xie, Guangxi Zhu, et, al. Robust Vehicles Extraction in a Videobased Intelligent Transportation Systems, IEEE 2005 International Conference on Communications, Circuits and Systems, Vol. (2) 27-30, May, 2005.
59. C. Jiang and M.O. Ward, "Shadow identification," Proceedings of IEEE Int'l Conference on Computer Vision and Patten Recognition, pp. 606-612, 1992.
60. Cucchiara R. et al., "Improving shadow suppression in moving object detection with HSV color information," IEEE Proc. International Conference on Intelligent Transportation Systems, pp.334-339, 2001.
61. Mikic I. et al., Moving shadow and object detection in traffic scenes," IEEE Proc. International Conference on Pattern Recognition, vol.1, pp.321-324, 2000.