Tracking articulated human movements witha component based approach to boosted multiple instance learning

Our work is about a new class of object trackers that are based on a boosted Multiple Instance Learning (MIL) algorithm to track an object in a video sequence. We show how the scope of such trackers can be expanded to the tracking of articulated movements by humans that frequently result in large frame-to-frame variations in the appearance of what needs to be tracked. To deal with the problems caused by such variations, our paper presents a component based version of the boosted MIL algorithm. Components are the output of an image segmentation algorithm applied to the pixels in the bounding box encapsulating the object to be tracked. The components give the boosted MIL the additional degrees of freedom that it needs in order to deal with the large frame-to-frame variations associated with articulated movements.