Extended Visual Tracking for Video Analytics under the Bayesian Probabilistic Graphical Framework
- Day - Time: 15 February 2013, h.11:30
- Place: Area della Ricerca CNR di Pisa - Room: C-29
- Mauricio Soto Alvarez (Technical Research Centre of Finland)
Visual tracking represents the basic processing step for most Video Analytics applications where the aim is to automatically understand the actions occurring in a monitored scene. Consequently, the performances of these applications are significantly dependent on the accuracy and robustness of the tracking algorithm. Bayesian state estimation and Probabilistic Graphical Models (PGMs) have proved to be very powerful and appropriate mathematical tools to efficiently solve the inference problem of motion estimation by combining object dynamics and observations. In this seminar, an efficient algorithm for Extended Visual Object Tracking (EVOT) is shown. The word extended refers to the fact that the tracker exploits multiple measurements yield by each target. In particular, the presented technique integrates local interest points with global color in order to provide a rich representation of the target and a robust tracker developed under the Bayesian Probabilistic Graphical Model framework.