Research And Implementation Of Human Abnormal Behavior Analysis In Video Surveillance

Intelligent Video Surveillance (IVS) is a kind of security technology to automate objectdetection, tracking, recognition, and activity analysis in image sequences, which are captured bycamera. The analysis and recognition of human abnormal behavior of IVS, which are being moreand more popular, can not only ignore a large of useless information, but also save a lot of humanand material resources. Its research object is the image sequences captured by camera, and it aims atunderstanding the object's action based on object detection, tracking and recognition.This paper studies simple human abnormal behavior of IVS such as crossing line, abandonedobject and hovering. The main contributions of this thesis are summarized as follow:1. Make a further study on the algorithm of object detection, tracking and recognition.2. Make a further study on the research method of motion analysis and motion trajectoryanalysis. Research on motion object analysis includes the descriptive method of object area andobject, and the motion feature of the object. Research on motion trajectory analysis includes themotion trajectory's representation based on distance-direction angle, and the solving method ofmain direction angle.3. This paper gives the recognition method of achieving the abnormal detection such ascrossing line and abandoned object, and recognizing the simple human abnormal behavior hovering,relying on activity analysis based on motion space and the motion feature proposed by the motionanalysis and motion trajectory analysis. Furthermore, improve the background update algorithm inhuman abnormal behavior of abandoned object.At last, this paper accomplishes the algorithm in the environment of VC++6.0 and OpenCV,and analysis the experiment results. Experimental results certify that the algorithm can fast andaccurately achieve the abnormal detection such as crossing line and abandoned object, andrecognize the simple human abnormal behavior hovering in image sequences. In addition thealgorithm can meet the needs of real-time of the system.