A MultiView Appearance Model for people re-identification

Tracking moving objects across events that break the continuity of the trajectory, such as occlusions or temporary exits from the scene, usually requires that a model of the objects of interest is created and maintained. Commonly used model representations are prone to errors when the objects can change the direction of their motion. In this paper we introduce a novel model representation, the MultiView Appearance Model, specifically devised to deal with the issue. The algorithms that create and update the model also take into account the problem of object apparent size changes due to perspective. An experimental evaluation of the proposed model representation has been performed on the PETS2010 dataset. The results show a consistent improvement of the performances in comparison with another well-known appearance model.