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Most sparse linear representation-based trackers need to solve acomputationally expensive L1-regularized optimization problem. To address thisproblem, we propose a visual tracker based on non-sparse linearrepresentations, which admit an efficient closed-form solution withoutsacrificing accuracy. Moreover, in order to capture the correlation informationbetween different feature dimensions, we learn a Mahalanobis distance metric inan online fashion and incorporate the learned metric into the optimizationproblem for obtaining the linear representation. We show that online metriclearning using proximity comparison significantly improves the robustness ofthe tracking, especially on those sequences exhibiting drastic appearancechanges. Furthermore, in order to prevent the unbounded growth in the number oftraining samples for the metric learning, we design a time-weighted reservoirsampling method to maintain and update limited-sized foreground and backgroundsample buffers for balancing sample diversity and adaptability. Experimentalresults on challenging videos demonstrate the effectiveness and robustness ofthe proposed tracker.

Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning