Online pedestrian tracking via saliency-based H-S histogram

Object tracking is one of the most important topics in computer vision. While the state-of-the-art tracking algorithms achieved great success, there are still some challenging problems to be solved. Firstly, it remains a tough task to develop a tracking algorithm with both accuracy and efficiency. Secondly, the ground truth is often given by a rectangular bounding box, which contains not only the target, but also the background pixels. The background pixels in the initial bounding box will mislead the appearance model of the target. Thirdly, most features for representing the target only use the gray scale information, and are not robust to pedestrians. The third problem is especially serious when the targets are pedestrians, which have colorful cloths on them, and change their pose and shape while walking. In this paper, we propose a novel saliency-based H-S histogram algorithm. Saliency detection can efficiently delete most of the background pixels, which makes the appearance model more accurate. H-S histogram is a statistic-based feature, which is more robust to shape deformation, and the color information is considered in the H-S channels. Experiments on Caltech pedestrian database show the proposed method can handle some hard cases, and achieves a higher success rate.