Compressed Video Anomaly Detection of Human Behavior Based on Abnormal Region Determination

Abstract

Video anomaly detection has a wide range of applications in video monitoring-related scenarios. The existing image-domain-based anomaly detection algorithms usually require completely decoding the received videos, complex information extraction, and network structure, which makes them difficult to be implemented directly. In this article, we focus on anomaly detection directly for compressed videos. The compressed videos need not be fully decoded and auxiliary information can be obtained directly, which have low computational complexity. We propose a compressed video anomaly detection algorithm based on accurate abnormal region determination (ARD-VAD), which is suitable to be deployed on edge servers. First, to ensure the overall low complexity and save storage space, we sparsely sample the prior knowledge of I-frame representing the appearance information and motion vector (MV) representing the motion information from compressed videos. Based on the sampled information, a two-branch network structure, which consists of MV reconstruction branch and future I-frame prediction branch, is designed. Specifically, the two branches are connected by an attention network based on the MV residuals to guide the prediction network to focus on the abnormal regions. Furthermore, to emphasize the abnormal regions, we develop an adaptive sensing of abnormal regions determination module based on motion intensity represented by the second derivative of MV. This module can enhance the difference of the real anomaly region between the generated frame and the current frame. The experiments show that our algorithm can achieve a good balance between performance and complexity.