Deformable multiple-kernel based human tracking using a moving camera

In this paper, we propose an innovative human tracking algorithm, which efficiently integrates the deformable part model (DPM) into the multiple-kernel based tracking using a moving camera. By representing each part model of a DPM detected human as a kernel, the proposed algorithm iteratively mean-shift the kernels (i.e., part models) based on color appearance and histogram of gradient (HOG) features. More specifically, the color appearance features, in terms of kernel histogram, are used for tracking each body part from one frame to the next, the deformation cost provided by DPM detector is further used to constrain the movement of each body kernel based on the HOG features. The proposed deformable multiple-kernel (DMK) tracking algorithm takes advantage of not only low computation owing to the kernel-based tracking, but also robustness of the DPM detector. Experimental results have shown the favorable performance of the proposed algorithm, which can successfully track human using a moving camera more accurately under different scenarios.