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.

► In this paper we present a non-parametric approach to anomaly detection in surveillance videos. ► The real-time system uses spatio-temporal features, integrated in a multi-scale approach. ► The system can localize anomalies temporally (at frame level) and spatially (within frame). ► The systems has been compared to state-of-the-art approaches on a real-world UCSD dataset. ► According to experiments our method consistently outperforms other real-time approaches.