Abstract: Recent years, with the widespread use of video surveillance technology, video anomaly detection, which can intelligently analyze massive videos and quickly discover the abnormalities, has received wide attention. This paper aims to give a comprehensive survey on deep learning based video anomaly detection methods. Firstly, a brief introduction of video anomaly detection is given, including the basic concepts, basic tasks, modeling process, learning paradigms as well as the evaluation perspectives. Secondly, the video anomaly detection methods are classified into four categories: reconstruction-based, prediction-based, classification-based, and regression-based. Their basic modeling ideas, typical algorithms, advantages, and disadvantages are discussed in detail.  On this basis, the commonly used single-scene video anomaly detection public datasets and evaluation indicators are introduced, and the performance of representative anomaly detection algorithms is compared and analyzed. Finally, summary is conducted, and the future development directions related to datasets, algorithm and evaluation criteria of video anomaly detection are proposed.

Key words: surveillance video, anomaly detection, deep learning, single scene, learning paradigm