Robust multi-camera 3D tracking from mono-camera 2d tracking using Bayesian Association

Visual tracking is essential for automatic scene understanding and surveillance of areas of interest. Monocular 2D tracking has been largely studied in the literature, but it usually provides inadequate or incomplete information for event interpretation. In addition, it proves insufficiently robust, due to view-point limitations and lack of depth information. However, the association of multiple cameras with overlapped fields of view allows the inference of 3D information and, thus, a richer description of the monitored scene.