Abnormal Behavior Recognition Based on Trajectory Feature and Regional Optical Flow

In order to recognize the human abnormal behavior by the video monitoring system, this paper proposes an abnormal behavior recognition method of trajectory characteristics and regional optical flow based on the characteristics of the two kinds. By adopting a modified hybrid Gauss model for background modeling, the moving foreground in video is extracted using the background subtraction method. The 8-adjacent connection area labeling method is used to label the foreground region so as to obtain the regional center trajectory. The Lucas-Kanade algorithm is used to extract the optical flow information within the movement region, and the regional flow features are described by the histogram with the weighted amplitude direction. The abnormal pedestrian behavior is identified through the analysis of the target trajectory and the entropy of histogram in the computational region. The modified mixed Gauss background model can effectively remove the interference factors and environmental disturbance on the foreground extraction so as to improve illumination changes. The trajectory analysis is done prior to the regional flow analysis if specific situation occurs, overcoming some problems such as the existing high misdiagnosis rate only from the trajectory feature recognition, the failure of individual existence through the optical flow feature recognition, the large amount of computation, etc. The experiments show that the proposed method can effectively identify the specific human abnormal behaviors.