Discriminative local collaborative representation for online object tracking

Sparse representation has been widely applied to object tracking. However, most sparse representation based trackers only use the holistic template to encode the candidates, where the discriminative information to separate the target from the background is ignored. In addition, the sparsity assumption with the l1 norm minimization is computationally expensive. In this paper, we propose a robust discriminative local collaborative (DLC) representation algorithm for online object tracking. DLC collaboratively uses the local image patches of both the target templates and the background ones to encode the candidates by an efficient local regularized least square solver with the l2 norm minimization, where the feature vectors are obtained by employing an effective discriminative-pooling method. Furthermore, we formulate the tracking as a discriminative classification problem, where the classifier is online updated by using the candidates predicted according to the residuals of their local patches. To adapt to the appearance changes, we iteratively update the dictionary with the foreground and background templates from the current frame and take occlusions into account as well. Experimental results demonstrate that our proposed algorithm performs favorably against the state-of-the-art trackers on several challenging video sequences.