Review for Anomaly Detection in Video Surveillance System Based on Deep Learning

Abstract

In this paper, abnormal target detection and location in video surveillance system are studied. In recent years, with the rapid development of network information technology, video surveillance technology has been widely used, artificial anomaly detection methods have no way to meet the effective growth of video surveillance data, with 3D technology, face recognition technology, etc., also promote the development of the field of computer vision, for the rapid analysis of a large number of video data to provide effective support. At present, abnormal target detection methods in video surveillance system mainly include the following two methods: One is to extract two-dimensional data features from video surveillance data, and effectively express video targets according to the extracted features. The information expressed mainly includes time information and spatial information. The second is to directly learn 3D space-time features for the module with motion information to detect the location of the abnormal target. Finally, the paper summarizes the full text and looks forward to the future development direction of video anomaly detection from three aspects: data set, method and evaluation index.