Online discriminative dictionary learning via label information for multi task object tracking

In this paper, a supervised approach to online learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a compact and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multi-classifier simultaneously. Combined with multi task sparse learning, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between multi task sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy and robustness.