Detecting Contextual Anomalies by Discovering Consistent Spatial Regions

• URL: http://arxiv.org/abs/2501.08470v1
• Date: Tue, 14 Jan 2025 22:33:07 GMT
• Title: Detecting Contextual Anomalies by Discovering Consistent Spatial Regions
• Authors: Zhengye Yang, Richard J. Radke,
• Abstract summary: 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. We demonstrate this approach, using orders of magnitude fewer parameters than competing models, in the challenging spatial-context-dependent Street Scene dataset.
• Score: 5.160649627495959
• License:
• Abstract: 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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