BI-CN/14-15-007: Research on Abnormal Behavior Detection and Warning in Real-time Video Surveillance Based on Multimedia Algorithms
With increasing importance of social public security and rapid development of video surveillance, social public security depends more and more on video image surveillance systems. Video image surveillance system has become important indispensable infrastructure for public security. Traditional video surveillance systems only displayed and recorded video information. Methods of data processing are done by real-time manual monitoring or manual postprocessing. Real-time analysis cannot be done, especially during the unexpected or abnormal events. Moreover, data amount accumulates to terabytes of recoreded video heads. Because of large amounts of video heads, it is difficult to find abnormal events or accidents in reasonable time, whilstquerying useful information from the video database.
In order to eliminate any potential accident danger and handle the events without any delay, we applied this project proposal. We will extract key data from video surveillance system and analyze important information. We will put our emphasis on state-of-the-art technology and algorithms for real-time video surveillance that take the advantages of image processing and machine learning. Our goal is to automatically analyze real-time video information input, extract the foreground images and update background images, detection and tracking of moving targets, analysis of the tracking targets, and perform an early warning in case of abnormal behavior. This is the initiative and intelligence of video surveillance system. These algorithms will provide 24 hours real-time monitoring and intelligent analysis of the captured information. In case of abnormal cases, the system will provide an alarm in time to avoid possible accidents. Hence, such system will save material and financial resources required when employing monitor workers.