Human tracking using joint color-texture features and foreground-weighted histogram

This paper proposes a new appearance model for human tracking based on Mean Shift framework. The proposed method uses a novel target representation by using joint Color-Texture features and Foreground-Weighted Histogram (CTFWH) for a more distinctive and effective target representation. Our contribution is threefold: firstly, to exploit the texture information of the target, we have used joint color-texture histogram to represent the target. Local Binary Pattern (LBP) technique is employed to identify texture features in the target region. Secondly, we have proposed a representation model of the foreground region named Foreground-Weighted Histogram (FWH), in order to exploit the significant features of the foreground region and to use it for selecting only the salient parts from the target model. Thirdly, we propose a simple method to update the foreground model due to the important foreground changes over the tracking process. Hence, by combining these concepts we generate new features for target representation and human tracking. The proposed method is designed for human tracking in complex scenarios and tested for comparative results with existing state-of-the-art algorithms. Experimental results on numerous challenging video sequences verify the significance of the proposed approach in terms of robustness and performance to complex background, illumination and appearance changes, similar target and background appearance, presence of distractors, target and camera motion, occlusions and large background variation.