Research Progress of Video Anomaly Detection Technology

Abstract

Video anomaly detection refers to the detection and identification of events that deviate from normal behavior, which has a wide range of applications in surveillance video. In this paper, the video anomaly detection algorithm based on deep learning is investigated in depth and summarized comprehensively. Firstly, this paper analyzes the related content of video anomaly detection and the challenges faced by anomaly detection, then introduces and analyzes the related algorithms of video anomaly detection from three aspects: supervised, semi-supervised and unsupervised. The algorithms in three different scenarios are further refined and classified. The algorithms in the supervised scenario are divided into two types: binary classification and multi-classification. The algorithms in the semi-supervised scenario are divided into two types: calculating anomaly scores and clustering discrimination. The algorithms in the unsupervised scenario are divided into two types: reconstruction discrimination and prediction discrimination. The characteristics, advantages and disadvantages of different technologies are analyzed. The commonly used datasets in the field of video anomaly detection and the evaluation criteria of detection performance are introduced, and the performance of current mainstream video anomaly detection algorithms is compared and analyzed. Finally, the future research direction of video anomaly detection algorithm is discussed and prospected.

Keywords

• |deep learning|anomaly detection|supervised|semi-supervised|unsupervised