2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
Thien Huynh-The , Department of Computer Engineering, Kyung Hee University, Gyeonggi-do, 446-701, Korea
Dinh-Mao Bui , Department of Computer Engineering, Kyung Hee University, Gyeonggi-do, 446-701, Korea
Sungyoung Lee , Department of Computer Engineering, Kyung Hee University, Gyeonggi-do, 446-701, Korea
Yongik Yoon , Department of Multimedia Science, Sookmyung Women's University, Seoul, 140-172, Korea
Recently CCTV-based behavior recognition have gained considerable attention in the transportation surveillance systems to identify normalities, such as traffic jams, accidents, and dangerous driving. An improved method is presented in this paper for the traffic behavior surveillance system by discovering more highly specific features based on the trajectory information. The multiple sparse feature comprising the object location, moving direction, speed, and appearance time length obtained from the moving object detection and tracking stage is modeled by the Pachinko Allocation Model. This hierarchical probabilistic model captures the correlation among the traffic activities and behaviors through the sparse features as the visual words. In the classification phase, the Support Vector Machine constructed from Decision Tree Architecture is utilized. Compared with existing methods, the proposed method outperforms 3-8% approximately in overall classification accuracy.
Hidden Markov models, Trajectory, Surveillance, Feature extraction, Correlation, Resource management, Support vector machines
T. Huynh-The, D. Bui, S. Lee and Y. Yoon, "Improved PAM-based traffic behavior recognition using trajectory-wise features," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 257-260.