Non-parametric anomaly detection exploiting space-time features
Lorenzo Seidenari, Marco Bertini · 2010
In this paper a real-time anomaly detection system for video streams is proposed. Spatio-temporal features are exploited to capture scene dynamic statistics together with appearance. Anomaly detection is performed in a non-parametric fashion, evaluating directly local descriptor statistics. A method to update scene statistics, to cope with scene changes that typically happen in real world settings, is also provided. The proposed method is tested on publicly available datasets.