Vehicle behavior understanding based on movement string

This paper proposes an analysis method based on Movement string for behavior understanding. Trajectories are analyzed by the improved principal component analysis (PCA) method which introduces the trajectory location and direction. Trajectory location and direction are the main features of PCA for scene division and Gaussian Mixture Hidden Markov Model. With the help of these two features, we can recognize action and can identify abnormal event. Movement string is defined and analyzed to get the semantic feature of vehicle. From the four rules presented in the paper, we can infer the behavior and describe it by the natural language. Finally, through some experiments, we picked the best initial parameters of HMM for training purpose. Further we put experiments on actual scene and found the recognition rate 88.6%. Results authenticate the accuracy of behavior understanding.

[1]  S. Khalid,et al.  Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients , 2005, VSSN@MM.

[2]  Chao Li,et al.  Real-time Detection of Abnormal Vehicle Events with Multi-Feature over Highway Surveillance Video , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[3]  Nikolaos Papanikolopoulos,et al.  Combining multiple tracking modalities for vehicle tracking at traffic intersections , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[4]  Tieniu Tan,et al.  A hierarchical self-organizing approach for learning the patterns of motion trajectories , 2004, IEEE Trans. Neural Networks.

[5]  Dan Schonfeld,et al.  Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models , 2007, IEEE Transactions on Image Processing.

[6]  Tieniu Tan,et al.  Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[7]  Mohan M. Trivedi,et al.  A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Hao Sheng,et al.  An approach to detecting abnormal vehicle events in complex factors over highway surveillance video , 2008 .

[9]  W. Eric L. Grimson,et al.  Learning Semantic Scene Models by Trajectory Analysis , 2006, ECCV.

[10]  Gian Luca Foresti,et al.  Trajectory-Based Anomalous Event Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Dan Schonfeld,et al.  Segmented trajectory based indexing and retrieval of video data , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).