The Design And Implementation Of An Abnormal Behavior Detection System Based On Video

The main research of this thesis is to extract the abnormal situation of the classroom video such as handing-up, sleep in the classroom and so on, and to detect and identify faces on the corridor video in abnormal region. According to their own research direction and the actual situation of the school, we did a detailed analysis of the system users (including teachers, students, parents of students, etc.), and determined the need to achieve the function. A detailed study of the needs analysis, I designed the classroom video anomaly detection system, corridor video anomaly detection system's framework and the realization of function modules. The two systems are implemented by using OpenCV2.4.4 and MFC in the framework of the VS2010 provided by Microsoft.Anomaly detection in the classroom video is mainly used for moving object extraction. In this paper, three frame difference methods are adopted to realize the background separation and foreground extraction. In the actual situation of the school environment and equipment condition, the three frame difference method is used to detect the abnormal objects, such as, high performance of the complex classroom environment.School corridor video anomaly detection, is to test abnormal people who do some dangerous things during the break time and extract their facial pictures to be manipulated. Furthermore by comparison with our acquisition of university people face database. We can extract their individual information, such as class?grade and name. This system mainly uses the image processing function of OpenCV2.4.4 to carry on the image processing, and calls its built-in face detection function to carry on the face detection. And we use the HOG feature to extract the face feature, train SVM classifier, and realize the face recognition. In the experiment test, the system test and detect the identified face, to match it with data in the face database.Through the experimental analysis, classroom video anomaly detection system and corridor detection system are proved to be stable and be able to detect the abnormal regions in the video.