通过聚类物体属性发现一致空间区域，实现高效视频异常检测。
Detecting Contextual Anomalies by Discovering Consistent Spatial Regions
- 用高斯混合模型聚类物体属性，挖掘共享活动的空间区域。
- 参数量少一个数量级，仍达到街景数据集最优性能。
- 生成可解释的正常性热图，无需预训练分割模型。
我们提出一种建模空间上下文的方法，用于视频异常检测。核心思路是利用高斯混合模型对联合物体属性进行聚类，发现具有相似物体级活动的空间区域。实验表明，该方法在参数量比现有模型少一个数量级的情况下，在具有挑战性的空间上下文依赖型街景数据集（Street Scene）上达到了当前最优性能。此外，模型学习到的高分辨率空间区域还为人类操作员提供了无需预训练分割模型的可解释正常性地图。
原文摘要 · Abstract (English)
We describe a method for modeling spatial context to enable video anomaly detection. The main idea is to discover regions that share similar object-level activities by clustering joint object attributes using Gaussian mixture models. We demonstrate that this straightforward approach, using orders of magnitude fewer parameters than competing models, achieves state-of-the-art performance in the challenging spatial-context-dependent Street Scene dataset. As a side benefit, the high-resolution discovered regions learned by the model also provide explainable normalcy maps for human operators without the need for any pre-trained segmentation model.
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