Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection

• URL: http://arxiv.org/abs/2403.19111v1
• Date: Thu, 28 Mar 2024 03:07:16 GMT
• Title: Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection
• Authors: Hao Shen, Lu Shi, Wanru Xu, Yigang Cen, Linna Zhang, Gaoyun An,
• Abstract summary: Video Anomaly Detection (VAD) aims to identify abnormalities within a specific context and timeframe. Recent deep learning-based VAD models have shown promising results by generating high-resolution frames. We propose a self-supervised learning approach for VAD through an inter-patch relationship prediction task.
• Score: 19.643936110623653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Anomaly Detection (VAD), aiming to identify abnormalities within a specific context and timeframe, is crucial for intelligent Video Surveillance Systems. While recent deep learning-based VAD models have shown promising results by generating high-resolution frames, they often lack competence in preserving detailed spatial and temporal coherence in video frames. To tackle this issue, we propose a self-supervised learning approach for VAD through an inter-patch relationship prediction task. Specifically, we introduce a two-branch vision transformer network designed to capture deep visual features of video frames, addressing spatial and temporal dimensions responsible for modeling appearance and motion patterns, respectively. The inter-patch relationship in each dimension is decoupled into inter-patch similarity and the order information of each patch. To mitigate memory consumption, we convert the order information prediction task into a multi-label learning problem, and the inter-patch similarity prediction task into a distance matrix regression problem. Comprehensive experiments demonstrate the effectiveness of our method, surpassing pixel-generation-based methods by a significant margin across three public benchmarks. Additionally, our approach outperforms other self-supervised learning-based methods.

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