Visual tracking in high-dimensional particle filter.

In this paper, we propose a novel object tracking algorithm by using high-dimensional particle filter and combined features. Firstly, the refined two-dimensional principal component analysis and the tendency are combined to represent an object. Secondly, we present a framework using high-order Monte Carlo Markov Chain which considers more information and performs more discriminative and efficient on moving objects than the traditional first-order particle filtering. Finally, an advanced sequential importance resampling is applied to estimate the posterior density and obtains the high-quality particles. To further gain the better samples, K-means clustering is used to select more typical particles, which reduces the computational cost. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the performance of our proposed algorithm is superior to the state-of-the-art methods.