Human Tracking in Crowded Scenes Using Integration of Three Kinds of Models and Adaptive Learning

Abstract

Human tracking in crowded scenes is a challenging problem because of frequent occlusion and the presence of similar regions. In this paper, we propose an online human tracking method which can handle occlusion and targets with similar regions. Our method compares the target region with a surrounding region and targets with similar regions at current frame. In addition, we also compare the target region at current and previous frames. We reduce the probabilities of uncommon colors at current and previous frames, and the tracking accuracy is improved. The effectiveness of the proposed method has been demonstrated by comparison with state-of-the-art trackers on the PETS2009 dataset.