Human Tracking In Crowded Scenes Using Target Information At Previous Frames

Human tracking in crowded scenes is a challenging problem because of frequent occlusion and presence of the tracking in 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 thereby improving the tracking accuracy. The effectiveness of the proposed method has been demonstrated via comparison with state-of-the-art trackers on the PETS2009 dataset.