Finding behavioural anomalies in public areas using video surveillance data

In this paper we propose an approach for detecting anomalies in data from visual surveillance sensors. The approach includes creating a structure for representing data, building ldquonormal modelsrdquo by filling the structure with data for the situation at hand, and finally detecting deviations in the data. The approach allows detections based on the incorporation of a priori knowledge about the situation and on data-driven analysis. The main advantages with the approach compared to earlier work is the low computational requirements, iterative update of normal models and a high explainability of found anomalies. The proposed approach is evaluated off-line using real-world data and the results support that the approach could be used to detect anomalies in real-time applications.