In this paper we propose dense spatio-temporal features to capture scene dynamic statistics together with appearance, in video surveillance applications. These features are exploited in a real-time anomaly detection system. Anomaly detection is performed using a non-parametric modelling, evaluating directly local descriptor statistics, and an unsupervised 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 proposed method is tested on publicly available datasets and compared to other state-of-the-art approaches.
Dense Spatio-temporal Features For Non-parametric Anomaly Detection And Localization / L. Seidenari;M. Bertini;A. D. Bimbo. - STAMPA. - (2010), pp. 27-32. (Intervento presentato al convegno ACM Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (ARTEMIS) nel 2010-Oct).
Dense Spatio-temporal Features For Non-parametric Anomaly Detection And Localization
SEIDENARI, LORENZO;BERTINI, MARCO;DEL BIMBO, ALBERTO
2010
Abstract
In this paper we propose dense spatio-temporal features to capture scene dynamic statistics together with appearance, in video surveillance applications. These features are exploited in a real-time anomaly detection system. Anomaly detection is performed using a non-parametric modelling, evaluating directly local descriptor statistics, and an unsupervised 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 proposed method is tested on publicly available datasets and compared to other state-of-the-art approaches.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.