Centroid iteration image tracking algorithm

In order to avoid the unreasonable kernel weight imposed on images by mean-shift tracking algorithm and the tracking bias, a maximum posterior probability measure based centroid iteration tracking algorithm is developed. The computation property of maximum posterior probability similarity measure is first analyzed, based on which it is indicated that the contribution of each pixel to the similarity value can be calculated by this measure. On the basis of it, a non-kernel and non-parametric centroid iteration image tracking algorithm is proposed, which takes the similarity contribution of each pixel as density and the similarity value of the candidate as mass, and obtains the target region by moving iteratively to the next centroid from the initial target region centroid. Theoretical analysis and experimental results show that the new algorithm is non-kernel and needs no extraction computation, which reduces the computational complexity. Meanwhile, utilizing the depressing capability of maximum posterior probability measure on the background, the tracking precision can be greatly improved.