Multi-camera 3D person tracking with particle filter in a surveillance environment

In this work we present and evaluate a novel 3D approach to track single people in surveillance scenarios, using multiple cameras. The problem is formulated in a Bayesian filtering framework, and solved through sampling approximations (i.e. using a particle filter). Rather than relying on a 2D state to represent people, as is most commonly done, we directly exploit 3D knowledge by tracking people in the 3D world. A novel dynamical model is presented that accurately models the coupling between people orientation and motion direction. In addition, people are represented by three 3D elliptic cylinders which allow to introduce a spatial color layout useful to discriminate the tracked person from potential distractors. Thanks to the particle filter approach, integrating background subtraction and color observations from multiple cameras is straight-forward. Alltogether, the approach is quite robust to occlusion and large variations in people appearence, even when using a single camera, as demonstrated by numerical performance evaluation on real and challenging data from an underground station.