Tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty induced by camera switching, sudden dynamic change, and rapid motion. This paper proposes an ordered over-relaxation Hamiltonian Markov chain Monte Carlo (MCMC) based tracking scheme for abrupt motion tracking within 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. At the proposal step, the ordered over-relaxation method is adopted to draw the momentum item in order to suppress the random walk behavior induced by Gibbs sampling. In addition, the paper provides an adaptive step-size scheme to simulate the Hamiltonian dynamics in order to reduce the simulation error. The proposed tracking algorithm can avoid being trapped in local maxima with no additional computational burden, which is suffered by conventional MCMC based tracking algorithms. Experimental results reveal that the presented approach is efficient and effective in dealing with various types of abrupt motions compared with several alternatives.