Robust object tracking via superpixels and keypoints

Most of the part-based methods just use the initial appearance model and feature information of the object. When the object is affected by occlusion, deformation and illumination factors, these methods can not be stable to the tracking object. In this paper, a tracking method is proposed based on keypoint matching and superpixel matching. Our method not only uses the initial feature information of the object, but also uses the feature information between adjacent frames. We use the superpixel to over-segment the candidate region which can be obtained by voting between the globally matched feature points, and then construct superpixel descriptors. The similarity between superpixels is based on the distance of the superpixel feature descriptor. Eventually, the object is selected according to the superpixel vote. Furthermore, we use qualitative and quantitative evaluations to evaluate our method on 18 challenging image sequences. Experimental results show that the proposed method outperforms 6 state-of-the-art tracking algorithms.