Ordered over-relaxation based Langevin Monte Carlo sampling for visual tracking

Visual tracking is a fundamental research topic in computer vision community, which is of great importance in many application areas including augmented reality, traffic control, medical imaging and video editing. This paper presents an ordered over-relaxation Langevin Monte Carlo sampling (ORLMC) based tracking method within the Bayesian filtering framework, in which the traditional object state variable is augmented with an auxiliary momentum variable. At the proposal step, the proposal distribution is designed by simulation of the Hamiltonian dynamics. We first use the ordered over-relaxation method to draw the momentum variable which could suppress the random walk behavior in Gibbs sampling stage. Then, we leverage the gradient of the energy function of the posterior distribution to draw new samples with high acceptance ratio. The proposed tracking method could ensure that the tracker will not be trapped in local optimum of the state space. Experimental results show that the proposed tracking method successfully tracks the objects in different video sequences and outperforms several conventional methods.