Yuanyuan Zhang and Fuxiang Wang, Beihang University, China

Recently, visual tracking based on sparse principle component analysis has drawn much research attention. As we all know, principle component analysis (PCA) is widely used in data processing and dimensionality reduction. But PCA is difficult to interpret in practical application and all those principal components are linear combinations of all variables. In our paper, a novel visual tracking method based on sparse principal component analysis and L1 tracking is introduced, which we named the method SPCA-L1 tracking. We firstly introduce trivial templates of L1 tracking method, which are used to describe noise, into PCA appearance model. Then we use lasso model to achieve sparse coefficients. Then we update the eigenbasis and mean incrementally to make the method robust when solving different kinds of changes of the target. Numerous experiments, where the targets undergo large changes in pose, scale and illumination, demonstrate the effectiveness and robustness of the proposed method.

Visual tracking, sparse principal component analysis, particle filter