Anomaly detection in video with Bayesian nonparametrics
This addresses anomaly detection in video surveillance, but it appears incremental as it builds on existing Bayesian nonparametric methods.
The paper tackled anomaly detection in video by proposing a dynamic Bayesian nonparametric topic model, which outperformed a non-dynamic model in classification performance.
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.