Robust object tracking with boosted discriminative model via graph embedding
Recently discriminative based appearance model has achieved great success in modeling the object. It considers the tracking as the binary classification problem to separate the object from the background. However it neglects the object appearance and the object properties, it will encounter problems when the object lacks the features to separate it from the background. In this paper, we compensate the discriminative model with the object appearance through a unified graph-based constraint embedding framework. Volterra kernels approximation and log-Riemannian transformation are introduced to transform the discriminative model and object subspace learning problem into a graph embedding problem. Through the unified graph-based constraint embedding framework, the discriminative model is enhanced with the help of the object appearance. The effectiveness of our proposed approach is demonstrated in various experiments and quantitative evaluation using several challenging sequences.