Online anomaly detection in videos by clustering dynamic exemplars

We propose a non-parametric hierarchical event model to perform online anomaly detection in videos. A dynamic exemplar set is first used to represent observed event samples which updates itself every time when a new sample comes in. Upon this set, clusters are extracted to summarize the exemplars, offering a compact yet informative data structure for past event samples. Abnormal events are detected by both considering their dissimilarity with the model and low frequency. Experiments on real world crowd surveillance videos demonstrate the effectiveness and robustness of the proposed algorithm which shows reliable detection rates and low false alarms.