Research On Abnormal Behavior Monitoring Technology Based On Multi-Features

Intelligent video surveillance system is one of the key research subject in computer vision field in recent years, with the rapid development of domestic economy, a series of social problems gradually exposed, coupled with international rampant terrorism, the demand for intelligent video surveillance system is becoming more and more intense in security field. The traditional video monitoring system can only accomplish image transmission, and most of them are used for investigation only after the incidents that have occurred. If we want to carry out real-time monitoring and detect the emergencies in time via the traditional video surveillance system, it can only be accomplished by special duty way. Intelligent video surveillance system has overcome the disadvantages of traditional video surveillance system, using the technology such as image processing, pattern recognition, artificial intelligence, etc., and can identify and intelligently monitor the objects in video, and automatically detect the specified contents of the video. Face with the increasing complex social security environment as well as the gradual increase pressures of safety and security work, the demand of video surveillance is increasing, and intelligent video surveillance system is significant to practical applications.The main task of intelligent video surveillance system is to identify the human abnormal behavior occurred in video. The abnormal behavior monitoring technology is mainly studied for intelligent surveillance system. The application of intelligent video surveillance system and abnormal behavior of technology and research status are introduced and analyzed in detail.Moreover, comparing the current motion foreground extraction methods, a background modeling method is presented based on the modified hybrid Gauss model, and used to segment and extract the moving foreground. The experimental results show that this method can overcome the problem of the existing mixed Gauss background modeling method, for example, initializing quite slowly, background and foreground prone to fusion, etc.. The proposed method possesses strong resistance ability to interference of background factors, can extract the moving target in a complex scene rapidly and effectively.Besides, another key technology in the detection of abnormal behavior-- moving target tracking technology is to be explored in depth. Through the analysis of the existing algorithms, the main factors that interfere with the tracking of motion target are identified, and a moving object tracking algorithm based on Kalman filter is put forward so as to improve the stability and accuracy of tracking.Finally, an abnormal behavior recognition method based on multi-features is proposed. The target behaviors are recognized by comprehensive analysis of the trajectory feature existed in moving target and the regional flow feature of video sequence. The proposed method overcomes the limitation of the original method.The design and experimental study of algorithm are fulfilled. The results demonstrated that abnormal behavior monitoring based on multi-features is achieved combining with several key technologies proposed in this paper. Thes e technologies have many merits such as wide application range, good real-time performance, and better accuracy rate.