Video Anomaly Detection Methods: a Survey

Video abnormal behavior detection is a hot research topic in computer vision. It involves extracting temporal and spatial features from video content to determine the presence of abnormal events and their types within the video, as well as to localize the regions and time where anomalies occur. This paper systematically reviews and categorizes existing methods for video abnormal behavior detection based on supervised/unsupervised learning. This paper categorizes the supervised methods into methods based on deviation mean calculation and multimodal methods. For unsupervised methods, it summarizes various completely unsupervised approaches. Starting from the current mainstream modeling approaches, this paper gives a detailed explanation of deviation mean calculation methods, summarizes multimodal methods based on the utilization and processing of different modal features, and introduces completely unsupervised methods based on two training approaches. By comparing the network architectures of different models, this paper summarizes the test datasets, use cases, advantages, and limitations of various abnormal behavior detection models. Furthermore, it compares and evaluates models using benchmark datasets and common evaluation standards such as frame-level and pixel-level standards, and conducts intra-class comparisons based on performance results, followed by analysis of the outcomes. Lastly, it explores trends in video abnormal behavior detection through five directions: virtual synthetic datasets, multimodal large models, lightweight models, etc.