Detecting and indexing moving objects for Behavior Analysis by Video and Audio Interpretation

Alessia Saggese, Luc Brun, Mario Vento

Abstract

In this thesis a system for analyzing moving objects behavior for surveillance applications is proposed: videos are processed in order to extract and analyze moving objects trajectories for identifying abnormal trajectories, associated to abnormal behaviors. Whereas the information extracted from the videos are not sufficient or not sufficiently reliable, the proposed system is enriched by a module in charge of recognizing audio events of interest such as shoots, screams or broken glasses. Finally, all the extracted information are suitably stored in order to allow an efficient retrieval from the human operator. Five different standard datasets have been used for testing the different modules proposed in this thesis; the obtained results, both in terms of accuracy and computational efficiency, confirm the effectiveness and the real applicability of the proposed approach.

Keywords

Tracking; Video Surveillance; Clustering; Audio Surveillance
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