Online object tracking via bounded error distance

Chunjuan Bo, Junxing Zhang, Jianjun He, Yuli Han

Abstract: Online object tracking is a very important issue in computer vision and video processing, which has many potential applications. This paper presents a novel tracking method based on the proposed bounded error distance with subspace representation. First, the tracked object is assumed to be consisted of a principle component analysis (PCA) subspace and a bounded error distance is adopted to handle outliers. Then, the relationship between the proposed bounded error distance and the sparsity constraint is revealed, based on which the representation model is relaxed into a convex L1 regularization model. Finally, a tracking framework is developed by combining the proposed bounded error model with subspace representation within the particle filter scheme. We conduct many experiments on ten challenging image sequences and compare our tracker with eight state-of-the-art tracking methods. Both qualitative and quantitative evaluations on some challenging video clips show that our tracker performs better than other tracking algorithms.

External IDs:dblp:journals/ijon/BoZHH16