Adaptive Tracking of Non-Rigid Objects Based on Color Histograms and Automatic Parameter Selection

One of the main difficulties in visual tracking is to take into account appearance changes (not only of the target but also of or due to the scene, illumination for example). The use of a Bayesian framework is very flexible and has proven to be very efficient in visual tracking. Moreover, color or greylevel histograms allow to track an objet with a low computational cost. The recently proposed color-based trackers integrated in a probabilistic framework are efficient for a given application (face tracking for example) but can not be generalized easily, due to the initialization and the adjustment of the different tracker parameters that are dependent on the input sequence. This paper presents a method based on color integrated in a particle filter that allows to cope with some of the usual problems of visual tracking (occlusions, target appearance changes, changes in resolution or in illumination) and to adapt easily to different applications (tracking of structures in aerial imagery as well as football players). The novelty of the tracker is its ability to automatically regulate all the parameters needed for tracking, which makes it flexible and easily usable for different applications.