A novel incremental weighted PCA algorithm for visual tracking
Kailing Guo, Xiangmin Xu, Fuhao Qiu, Jiayong Chen · 2013
This paper addresses the drifting problem in online visual tracking. The tracking result is usually described by a bounding box, which inevitably contains background in the box and causes drifting. This paper tries to treat the background part and the truly target part discriminatively to reduce the effect of background. A novel incremental weighted PCA (IWPCA) algorithm is proposed. The most important contribution of this paper is an approximation method which limits the great and increasing computational cost, caused by the weighted form, to a constant. Therefore it is suitable for online tracking. Combined with particle filter, the proposed algorithm achieves superior results in several challenging video sequences in terms of stableness and accuracy, and greatly alleviates the drifting problem.