Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

• URL: http://arxiv.org/abs/2112.03649v2
• Date: Wed, 8 Dec 2021 04:34:47 GMT
• Title: Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
• Authors: Shoubin Yu, Zhongyin Zhao, Haoshu Fang, Andong Deng, Haisheng Su, Dongliang Wang, Weihao Gan, Cewu Lu, Wei Wu
• Abstract summary: A novel Motion Embedder (ME) is proposed to provide a pose motion representation from the probability perspective. A novel task-specific Spatial-Temporal Transformer (STT) is deployed for self-supervised pose sequence reconstruction. MoPRL achieves the state-of-the-art performance by an average improvement of 4.7% AUC on several challenging datasets.
• Score: 43.004613173363566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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