针对城市监控中异常事件时长不一的问题，提出多粒度时间建模方法。
TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance
- 设计多头交叉与自注意力结构，捕捉不同时间尺度的时空特征
- 在扩展的UCF-Crime数据集上实现高精度异常检测
- 已在真实城市监控系统中部署，支持实时分析
视频监控中的异常检测近年来受到研究界关注。异常事件在视频流中的持续时间各不相同，给学习特定事件的时间动态带来挑战。本文提出一种用于真实城市监控场景的时序粒度异常检测方法（TeG），通过在不同时间尺度上融合空间-时间特征来应对该问题。TeG模型采用多头交叉注意力块和多头自注意力块实现这一目标。此外，我们还扩展了UCF-Crime数据集，增加了与智慧城市研究相关的新异常类型。所提模型已在城市监控系统中部署并验证，在工业环境中实现了成功的实时检测结果。
原文摘要 · Abstract (English)
Anomaly detection in video surveillance has recently gained interest from the research community. Temporal duration of anomalies vary within video streams, leading to complications in learning the temporal dynamics of specific events. This paper presents a temporal-granularity method for an anomaly detection model (TeG) in real-world surveillance, combining spatio-temporal features at different time-scales. The TeG model employs multi-head cross-attention blocks and multi-head self-attention blocks for this purpose. Additionally, we extend the UCF-Crime dataset with new anomaly types relevant to Smart City research project. The TeG model is deployed and validated in a city surveillance system, achieving successful real-time results in industrial settings.
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