Robust Abrupt Motion Tracking via Adaptive Hamiltonian Monte Carlo Sampling

摘要

In this paper, we propose an adaptive Hamiltonian Monte Carlo sampling based tracking scheme within the Bayesian filtering framework. At the proposal step, the ordered over relaxation method is used to draw the momentum item for the joint state variable, which can suppress the random walk behavior. In addition, we design adaptive step-size based scheme to simulate the Hamiltonian dynamics in order to reduce the simulation error. The proposed method is compared with several state-of-the-art tracking algorithms. Extensive experimental results have shown its superiority in handling various types of abrupt motions compared to the traditional tracking algorithms.