Tracking of Abrupt Motion
using Wang-Landau Monte Carlo Estimation



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 methods to fail since they violate the motion smoothness constraint. To address this problem, we introduce the Wang-Landau algorithm that has been recently proposed in statistical physics, and integrate this algorithm 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 estimated 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 reveal that our approach efficiently samples the object's states even in a whole state space without loss of time. Therefore, it tracks the object of which motion is drastically changing, accurately and robustly.

paper thumbnail


ECCV 2008 paper. (pdf, 0.7MB) Poster. (pdf, 3.4MB)


1. Junseok Kwon, Kyoung Mu Lee. Tracking of Abrupt Motion using Wang-Landau Monte Carlo Estimation, European Conference on Computer Vision (ECCV), 2008

2. Junseok Kwon and Kyoung Mu Lee. Wang-Landau Monte Carlo-based Tracking Methods for Abrupt Motions, IEEE Transaction Pattern Analysis and Machine Intelligence (TPAMI), accepted for publication
[project page].


video. (wmv, 32MB)


code. (zip, 24MB) dataset. (zip, 37MB)


This research is supported in part by: