Articulated Human Movements Tracking Through Online Discriminative Learning

In this thesis, we present 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, we present a component-based MIL (CMIL) algorithm with boosted learning. The components are the output of an image segmentation algorithm and 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. Furthermore we explored two enhancements of the basic CMIL tracking algorithm. The first is based on an extended definition of positive learning samples for CMIL tracking. This extended definition can filter out false-positive learning samples in order to increase the robustness of CMIL tracking. The second enhancement is based on a combined motion prediction framework with the basic CMIL tracking for resolving issues arising from large and rapid translational human movements. The need for appropriate motion transition can be satisfied by probabilistic modeling of motion. Experimental results show that the proposed approaches yield robust tracking performances in various tracking environments, such as articulate human movements as well as ground human movements observed from aerial vehicles