Multi-scale and real-time non-parametric approach for anomaly detection and localization

In this paper we propose an approach for anomaly detection and localization, in video surveillance applications, based on spatio-temporal features that capture scene dynamic statistics together with appearance. Real-time anomaly detection is performed with an unsupervised approach using a non-parametric modeling, evaluating directly multi-scale local descriptor statistics. A method to update scene statistics is also proposed, to deal with the scene changes that typically occur in a real-world setting. The proposed approach has been tested on publicly available datasets, to evaluate anomaly detection and localization, and outperforms other state-of-the-art real-time approaches.