Anomaly detection in video with Bayesian nonparametrics

Isupova, Olga, Kuzin, Danil, Mihaylova, Lyudmila Machine Learning 

A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper introduces a new abnormality measure for decision making. The proposed method is evaluated on both synthetic and real data. The comparison with a non-dynamic model shows the superiority of the proposed dynamic one in terms of the classification performance for anomaly detection.

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