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2008 International Conference on Computational Intelligence and Security
Multiple Feature Fusion for Tracking of Moving Objects in Video Surveillance
December 13-December 17
ISBN: 978-0-7695-3508-1
| ASCII Text | x | ||
| Huibin Wang, Chaoying Liu, Lizhong Xu, Min Tang, Xuewen Wu, "Multiple Feature Fusion for Tracking of Moving Objects in Video Surveillance," 2012 Eighth International Conference on Computational Intelligence and Security, vol. 1, pp. 554-559, 2008 International Conference on Computational Intelligence and Security, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/CIS.2008.86, author = {Huibin Wang and Chaoying Liu and Lizhong Xu and Min Tang and Xuewen Wu}, title = {Multiple Feature Fusion for Tracking of Moving Objects in Video Surveillance}, journal ={2012 Eighth International Conference on Computational Intelligence and Security}, volume = {1}, year = {2008}, isbn = {978-0-7695-3508-1}, pages = {554-559}, doi = {http://doi.ieeecomputersociety.org/10.1109/CIS.2008.86}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 Eighth International Conference on Computational Intelligence and Security TI - Multiple Feature Fusion for Tracking of Moving Objects in Video Surveillance SN - 978-0-7695-3508-1 SP554 EP559 A1 - Huibin Wang, A1 - Chaoying Liu, A1 - Lizhong Xu, A1 - Min Tang, A1 - Xuewen Wu, PY - 2008 KW - multiple features fusion KW - particle filter KW - vehicle tracking KW - video surveillance VL - 1 JA - 2012 Eighth International Conference on Computational Intelligence and Security ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CIS.2008.86
Recently video surveillance techniques have been widely applied to intelligent transportation systems. Tracking of moving objects such as vehicles has become a major topic in video surveillance applications. This paper presents a multi-feature fusion model based on a particle filter for moving object tracking. The particle filter combines color and edge orientation information by a stochastic fusion scheme. The scheme randomly selects single observation model to evaluate the likelihood of some particles. The stochastic selection probability is adjusted adaptively by the uncertainty associated with a feature model. The experiment shows that the proposed method has strong tracking robustness and can effectively solve the occlusion problem.
Index Terms:
multiple features fusion, particle filter, vehicle tracking, video surveillance
Citation:
Huibin Wang, Chaoying Liu, Lizhong Xu, Min Tang, Xuewen Wu, "Multiple Feature Fusion for Tracking of Moving Objects in Video Surveillance," cis, vol. 1, pp.554-559, 2008 International Conference on Computational Intelligence and Security, 2008
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