Using incremental subspace and contour template for object tracking

Abstract

Object tracking in the presence of appearance variation and occlusion is a hot topic in research, many algorithms were proposed in recent years. Early contour tracking algorithms used particle filter in a high dimensional space. In practice, contour points can move independently, hence contour deformation forms a high dimensional deformation space. As a result, the application of particle filter is calculation expensive. In this paper, we address the problem of tracking contour in complex environments by involving subspace and a contour template. Specifically, our algorithm tracks the global motion and the local contour deformation separately. We track the global motion by weighted distance to subspace, which is adaptive to the complex environment variation by incremental learning, and then use contour model to track local deformation and evolve the contour to the edge points. The experimental results show that our method can track object contour undergoing partially occlusion and shape deforming, which verify the effectiveness of the proposed algorithm.