Research On Video Anomaly Detection Method Based On Image Analysis

With the development of computer network technology and image processing technology, video surveillance system has been widely used in various industries, which provides an important guarantee for social security and stability. It is an urgent problem about the high efficiency management and intelligent warning of massive video surveillance,which is a hot research topic in computer vision technology.The traditional video anomaly detection algorithm mainly depends on the application of video specific scene knowledge and algorithm model, through the feature acquisition and processing of the video sequence, the anomaly detection is carried out. Because of the large types of anomalies and large differences, a single feature discriminant model is difficult to guarantee the accuracy of the algorithm. In this thesis, a method of video image analysis based on image processing is used to detect abnormal video objects in fixed scene, and the results are used to the abnormal depth detection.In this thesis, based on the theory of target recognition and video anomaly detection algorithm, a new method of video anomaly identification based on double level discrimination ability is proposed.Firstly,the method of multi-frame statistics is used to improve the frame difference method, and the operation of the ViBe algorithm is improved by increasing the pixel position judgment, and the two algorithms are combined to extract the foreground target effectively. Secondly,analyzing the characteristics of the overall shape and optical flow on regional target extraction. According to the different influence of different characteristics on decision, a pre-judgment model with combined judgment and multi-layer judgement is designed, which can do anomaly judgement fast and intuitively. Finally, in order to ensure the accuracy rate, the gradient feature and the optical flow feature are extracted from the target image whose prediction result is close to the anomaly, and the semi-supervised SVM discriminant model is constructed and discriminated. The experimental results show that this method can obtain the target contour well, play the guiding role of human knowledge, and timely and accurately find abnormal and early warning.