An adaptive mean shift tracking method using multiscale images

An adaptive mean shift tracking method for object tracking using multiscale images is presented in this paper. A bandwidth matrix and a Gaussian kernel are used to extend the definition of target model. The method can exactly estimate the position of the tracked object using multiscale images from Gaussian pyramid. The tracking method determines the parameters of kernel bandwidth by maximizing the lower bound of a log-likelihood function, which is derived from a kernel density estimate with the bandwidth matrix and the modified weight function. The experimental results show that it can averagely converge in 2.55 iterations per frame.