Robust tracking of humans and vehicles in cluttered scenes with occlusions

An algorithm for tracking multiple non-rigid objects in cluttered scenes is presented. The proposed approach models the shape of the objects by using corners. In particular, a learning algorithm is introduced in order to extract an adaptive model of the object automatically. The obtained adaptive model is used to individuate the object position and scale when occlusions are present. The method is used on an existing video-surveillance system in order to track moving objects in cluttered scenes. Results show that the proposed approach provides good performances with low processing times.