Efficient Visual Tracking via Hamiltonian Monte Carlo Markov Chain
Efficient visual tracking is a challenging task in the computer vision community due to its large motion uncertainty induced by occlusion, abrupt motion or appearance changes. In this paper, we propose a Hamiltonian Markov Chain Monte Carlo (MCMC) based tracking scheme for efficient tracking within the Bayesian filtering framework, aiming at handling full or partial occlusions, abrupt motion and appearance changes. In this tracking scheme, no complex models are built for motion uncertainties. The object states are augmented by introducing a momentum item and the Hamiltonian dynamics (HD) is integrated into the traditional MCMC-based tracking method. A new object state is proposed by computing a trajectory according to HD, implemented with the Leapfrog method. The new state can be distant from the current object state but, nevertheless, has a high acceptance probability, which consequently bypasses the slow exploration of the state space suffered by traditional random-walk proposal distribution. In addition, the proposed tracking algorithm can avoid being trapped in local maxima, which is suffered by conventional MCMC-based tracking algorithms. Experimental results reveal that our approach is efficient and effective in dealing with various types of tracking scenarios compared with several alternatives.
visual tracking monte carlo markov chain hamiltonian mcmc