Dense spatio-temporal features for nonparametric anomaly detection and localization

In this paper we propose dense spatio-temporal features to capture scene dynamic statistics together with appearance, in video surveillance applications. These features are ex-ploited in a real-time anomaly detection system. Anomaly detection is performed using a non-parametric modelling, evaluating directly local descriptor statistics, and an unsu-pervised or semi-supervised approach. A method to update scene statistics, to cope with scene changes that typically happen in real world settings, is also provided. The pro-posed method is tested on publicly available datasets and compared to other state-of-the-art approaches