Abstract

We propose a novel tracking algorithm based on the Wang- Landau Monte Carlo sampling method which efficiently deals with the abrupt motions. Abrupt motions could cause conventional tracking meth- ods to fail since they violate the motion smoothness constraint. To ad- dress this problem, we introduce the Wang-Landau algorithm that has been recently proposed in statistical physics, and integrate this algo- rithm into the Markov Chain Monte Carlo based tracking method. Our tracking method alleviates the motion smoothness constraint utilizing both the likelihood term and the density of states term, which is es- timated by the Wang-Landau algorithm. The likelihood term helps to improve the accuracy in tracking smooth motions, while the density of states term captures abrupt motions robustly. Experimental results re- veal that our approach efficiently samples the ob ject’s states even in a whole state space without loss of time. Therefore, it tracks the ob ject of which motion is drastically changing, accurately and robustly.