Detecting Contextual Anomalies by Discovering Consistent Spatial Regions
This work addresses the problem of detecting anomalies in video for surveillance or monitoring applications, representing an incremental improvement in efficiency and explainability.
The paper tackles video anomaly detection by modeling spatial context through clustering object-level activities, achieving state-of-the-art performance on the Street Scene dataset with significantly fewer parameters than competitors.
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.