Visual Tracking Based on Compressive Sensing MCMC Sampling
Lan Wang, Pingyang Dai, Yanlong Luo, Cuihua Li, Yi Xie · 2013
Real time visual tracking is a challenge problem in computer vision. In this paper, we propose a real-time tracking method based on compressive sensing Markov Chain Monte Carlo (MCMC) sampling. To extract the features of objects, non-adaptive random projections are employed in the object appearance model which adopts a very sparse random measurement matrix using compress sensing. These projection preserve the structure of objects in the image feature space. A Bayesian classifier is learnt from the object appearance model and the scores of this classifier are integrated into Markov Chain Monte Carlo acceptance mechanism. Furthermore, a two-stage tracking scheme is used to alleviate the drift problem. The experimental results demonstrate that the proposed method is real time and outperforms some start-of-the-art algorithms on public benchmark sequences in terms of accuracy and robustness.