Fusion of trajectory clusters for situation assessment

In this paper, we address the problem of identifying anomalous events in the context of a multi sensor surveillance system. Targets' trajectories are analyzed and compared to common patterns of activity represented as clusters of trajectories. Here we extend our previous work to cater for observations provided by multiple cameras observing the same scene. Data fusion is performed within the Dempster-Shafer theory of evidence framework. The proposed approach is validated through experimental results performed in the context of an automatic road traffic monitoring application