  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.
