Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

Abstract

Anomaly detection in surveillance videos is challenging and important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise. However, unlike pixel-based methods, which could directly exploit explicit motion features such as optical flow, pose-based methods suffer from the lack of alternative dynamic representation. In this paper, a novel Motion Embedder (ME) is proposed to provide a pose motion representation from the probability perspective. Furthermore, a novel task-specific Spatial-Temporal Transformer (STT) is deployed for self-supervised pose sequence reconstruction. These two modules are then integrated into a unified framework for pose regularity learning, which is referred to as Motion Prior Regularity Learner (MoPRL). MoPRL achieves the state-of-the-art performance by an average improvement of 4.7% AUC on several challenging datasets. Extensive experiments validate the versatility of each proposed module.

Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey

“…In contrast, surveillance using CSI can effectively provide activity recognition and behavior analysis without privacy violations [128]. At the same time, some video applications utilize the human skeleton to implement posture recognition and activity analysis without privacy issues [129]. Therefore, we can utilize skeleton recognition using CSI techniques to realize precise pose recognition in a public area.…”