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.