Nonlinear learning using LCC for online visual tracking

In this paper, we propose to address online visual tracking on the basis of Local Coordinate Coding (LCC), which integrates the advantages of the discriminative method and the generative method. In the discriminative module, a nonlinear function is trained using the local coordinate codes of image patches to identify the foreground patches from background. In the generative module, we introduce a similarity function that takes the spatial structures of local patches in the target into account between the candidate and holistic templates by reconstruction error. To deal with appearance change during tracking, an online update method is introduced. The proposed tracking method is evaluated on different challenging video sequences with center location error, and experimental results demonstrate the good performance of our method.