Online discriminative dictionary learning for visual tracking

Abstract

Dictionary learning has been applied to various computer vision problems, such as image restoration, object classification and face recognition. In this work, we propose a tracking framework based on sparse representation and online discriminative dictionary learning. By associating dictionary items with label information, the learned dictionary is both reconstructive and discriminative, which better distinguishes target objects from the background. During tracking, the best target candidate is selected by a joint decision measure. Reliable tracking results and augmented training samples are accumulated into two sets to update the dictionary. Both online dictionary learning and the proposed joint decision measure are important for the final tracking performance. Experiments show that our approach outperforms several recently proposed trackers.