用于弱监督视频异常检测的结构化证据选择

中文摘要 AI 辅助

弱监督视频异常检测仅依靠视频级标签进行训练，难以在复杂场景中准确定位异常事件。现实世界视频中，异常行为在外观和时间持续上变化大，且场景外观与动作动态常紧密纠缠，导致现有模型依赖场景统计线索而非真实行为偏差，检测性能不稳定。为此提出结构化证据选择框架（SESAD），将异常检测重构为基于片段级视觉证据的结构化推理过程。SESAD重组片段表示为语义结构化候选证据，在场景和动作约束下进行上下文条件选择，抑制场景干扰，减轻弱监督下的语义纠缠。还引入轻量级几何判别模块，通过相对几何关系进行异常决策。在多个数据集上实验表明，SESAD分别取得67.92、97.99和88.46的AUC，保持高计算效率和稳定的异常判别。

英文摘要

Weakly supervised video anomaly detection relies solely on video-level labels for training, making it difficult to accurately localize anomalous events in complex scenes. In real-world videos, anomalous behaviors exhibit large variations in appearance and temporal duration, while scene appearance and action dynamics are often tightly entangled. Consequently, existing models tend to rely on scene-related statistical cues rather than true behavioral deviations, resulting in unstable detection performance. To address this challenge, we propose a Structured Evidence Selection framework (SESAD) that reformulates anomaly detection as a structured reasoning process over clip-level visual evidence. Instead of directly mapping aggregated features to anomaly scores, SESAD reorganizes clip representations into semantically structured candidate evidence and performs context-conditioned selection under scene and action constraints. This mechanism adaptively emphasizes anomaly-relevant semantics while suppressing scene interference, thereby alleviating semantic entanglement under weak supervision. Furthermore, we introduce a lightweight geometric discrimination module that constructs a dual-prototype structure in the embedding space, enabling anomaly decisions through relative geometric relations. Extensive experiments on UBnormal, ShanghaiTech, and UCF-Crime show that SESAD achieves 67.92, 97.99, and 88.46 AUC, respectively, while maintaining high computational efficiency and overall consistently stable anomaly discrimination.

发表机构

• China University of Petroleum (East China)（中国石油大学（华东））
• The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））

机构由 AI 辅助整理，请以论文原文为准。