A Comprehensive Review for Video Anomaly Detection on Videos

Video Surveillance Systems (VSS) are widely utilized in public and private areas to increase public safety, such as shopping malls, markets, banks, hospitals, educational institutions, streets, and smart cities. The accuracy and fast identification of video anomalies is usually the major goal of security applications. However, because of varying environmental factors, the complexities of human activity, the ambiguous nature of the anomaly, and the absence of appropriate datasets, detecting video anomalies is challenging. This paper surveys the last three years, a comprehensive study of detecting video anomalies, and the recently used dataset. Moreover, a comparison study on different approaches has been performed, which are used for anomalies detection. We have noticed that deep learning has outperformed other methods in this field.