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Vehicle Detection and Tracking Using Difference Frame Image for Traffic Measurement System  

Kim, Hyung-Soo (School of Computer, Information, and Communication Engineering, Sangji University)
Hwang, Gi-Hyeon (Division of Computer Engineering, Dongse University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.17, no.1, 2016 , pp. 32-39 More about this Journal
Abstract
Intelligent Transport Systems (Intelligent Transportation System: ITS) is a system for inducing a flow of ideal car for using the most advanced technology, it is determined the status of the road, and take appropriate action. In order to be measured at various time points, and is managed, the information about the traffic situation is used image using a computer mainly. The image processing using a computer, it is an easy way to collect parameters of the various traffic in real time, technology has developed more and more. Vehicle detection of transport parameters of intelligent transportation system is a very important technology basically. Therefore, technology detection method using car background images and the contour line extraction method using an edge is used, however, problems have been raised on the accuracy of the detection rate.
Keywords
Intelligent transportation systems; vehicle detection; vehicle tracking; background difference image; Traffic Measurement System;
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1 Anagnostopoulos, Christos Nikolaos E., et al. "A license plate-recognition algorithm for intelligent transportation system applications," Intelligent Transportation Systems, IEEE Transactions on, Vol. 7, Issue 3, pp. 377-392, 2006.   DOI
2 Padmadas, M., et al. "A deployable architecture of Intelligent Transportation System-A developing country perspective," Computational Intelligence and Computing Research (ICCIC), IEEE International Conference on. IEEE, pp. 1-6, 2010.
3 G. Xu, J. Liu, Z. Tao, and X. Li, "The research and developmentof highway's electronic toll collection system," In Proc. of World Academy of Science Engineering Tech, pp. 359-362, 2007.
4 S. Araki, T. Matsuoka, H. Takemura, N. Yokoya, "Real-Time tracking of multiple moving object in moving camera imagesequences using robust statistics," IEEE International Conference on Pattern Recognition, Vol. 2, pp. 1433-1435, 1998.
5 Betke, Margrit, Esin Haritaoglu, and Larry S. Davis. "Real-time multiple vehicle detection and tracking from a moving vehicle," Machine Vision and Applications, Vol. 12, Issue 2, pp. 69-83, 2000.   DOI
6 J. K. Kang, Y. Son, Y. H. Yoon, S. Byun, "Regional Traffic Information Acquisition by Non-intrusive Automatic Vehicle Identification," The Journal of The Korea Institute of Intelligent Transport Systems, The Korea Institute of Intelligent Transport Systems, Vol 1, Issue 1, pp. 22-32, 2002.
7 A. Elgammal, R. Duraiswami, D. Harwood and L. S. Davis, "Background and foreground modeling using nonparameteric kernel density estimation for visual surveillance," In Processing of IEEE, Vol. 90, Issue 7, pp. 1151-1163, 2002.
8 Stauffer, Chris, and W. Eric L. Grimson. "Adaptive background mixture models for realtime tracking," Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.. Vol. 2. 1999.
9 Caraffi, Claudio, et al. "A system for real-time detection and tracking of vehicles from a single car-mounted camera," Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on. IEEE, pp. 975-982, 2012.
10 Hofmann, Martin, Philipp Tiefenbacher, and Gerhard Rigoll. "Background segmentation with feedback : The pixel-based adaptive segmenter," Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. IEEE, pp. 38-43, 2012.
11 Betke, Margrit, Esin Haritaoglu, and Larry S. Davis. "Multiple vehicle detection and tracking in hard real-time," Intelligent Vehicles Symposium, 1996., Proceedings of the 1996 IEEE, pp. 351-356, 1996.
12 Sivaraman, Sayanan, and Mohan M. Trivedi. "Real-time vehicle detection using parts at intersections," Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, pp. 1519-1524, 2012.
13 Collins, Robert T. "Mean-shift blob tracking through scale space," Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, Vol. 2, Issue 2, pp. 234-240, 2003.
14 Coifman, Benjamin, et al. "A real-time computer vision system for vehicle tracking and traffic surveillance," Transportation Research Part C: Emerging Technologies, Vol.6, Issue 4, pp. 271-288, 1998.   DOI