Discriminative Tracking by Metric Learning

We present a discriminative model that casts appearance modeling and visual matching into a single objective for visual tracking. Most previous discriminative models for visual tracking are formulated as supervised learning of binary classifiers. The continuous output of the classification function is then utilized as the cost function for visual tracking. This may be less desirable since the function is optimized for making binary decision. Such a learning objective may make it not to be able to well capture the manifold structure of the discriminative appearances. In contrast, our unified formulation is based on a principled metric learning framework, which seeks for a discriminative embedding for appearance modeling. In our formulation, both appearance modeling and visual matching are performed online by efficient gradient based optimization. Our formulation is also able to deal with multiple targets, where the exclusive principle is naturally reinforced to handle occlusions. Its efficacy is validated in a wide variety of challenging videos. It is shown that our algorithm achieves more persistent results, when compared with previous appearance model based tracking algorithms.