Probabilistic People Tracking for Occlusion Handling

This work presents a novel people tracking approach, able to cope with frequent shape changes and large occlusions. In particular, the tracks are described by means of probabilistic masks and appearance models. Occlusions due to other tracks, or due to background objects and false occlusions are discriminated. The classification is exploited in a selective model update. The tracking system is general enough to be applied with any motion segmentation module, it can track people interacting each other and it maintains the pixel assignment to track even with large occlusions. At the same time, the update model is very reactive, so as to cope with sudden body motion and silhouette’s shape changes. Due to its robustness, it has been used in many experiments of people behavior control in indoor situations.