基于Hamiltonian马氏链蒙特卡罗方法的突变运动跟踪

在计算机视觉领域，由镜头切换、目标动力学突变、低帧率视频等引起的突变运动存在极大的不确定性，使得突变运动跟踪成为该领域的挑战性课题.以贝叶斯滤波框架为基础，提出一种基于有序超松弛Hamiltonian马氏链蒙特卡罗方法的突变运动跟踪算法.该算法将Hamiltonian动力学融入MCMC（Markov chain Monte Carlo）算法，目标状态被扩张为原始目标状态变量与一个动量项的组合.在提议阶段，为抑制由Gibbs采样带来的随机游动行为，提出采用有序超松弛迭代方法来抽取目标动量项.同时，提出自适应步长的Hamiltonian动力学实现方法，在跟踪过程中自适应地调整步长，以减少模拟误差.提出的跟踪算法可以避免传统的基于随机游动的MCMC跟踪算法所存在的局部最优问题，提高了跟踪的准确性而不需要额外的计算时间.实验结果表明，该算法在处理多种类型的突变运动时表现出出色的处理能力.

Hamiltonian Markov Chain Monte Carlo Method for Abrupt Motion Tracking

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.