Hamiltonian Monte Carlo estimator for abrupt motion tracking

In this paper, we propose a Hamiltonian Markov Chain Monte Carlo based tracking algorithm for abrupt motion tracking within the Bayesian filtering framework. In this tracking scheme, the object states are augmented by introducing a momentum item and the Hamiltonian Dynamics (HD) is integrated into the traditional MCMC based tracking method. The HD has some excellent properties which are crucial in constructing MCMC updates. A new object state is proposed through constructing a trajectory according to HD, implemented using the Leapfrog method. And the state proposed by the HD can keep a certain distant from the current object state but nevertheless have a high acceptance probability, which consequently bypass the slow exploration of the state space suffered by traditional random-walk proposal distribution. Experimental results reveal that our approach is efficient and effective in handling various types of abrupt motions compared to several alternatives.