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.
Isupova et al. REF proposed a novel dynamic Bayesian nonparametric topic model and its Batch and online Gibbs samplers for anomaly detection in video.
Anomaly detection in video with Bayesian nonparametrics
arXiv: Machine Learning
Mathematics Computer Science