Structured and weighted multi-task low rank tracker

Low rank subspace and multi-task learning have been introduced into object tracking to pursuit the accurate representation. However, many existing methods regularize all rank components equally, and shrink with the same threshold. In addition, these methods ignore the discriminative and structured information among tasks during the tracking. In this paper, we propose an online discriminative multi-task tracker with structured and weighted low rank regularization (ODMT-SL). Specifically, the total tracking task is accomplished by the combination of multiple subtasks, and each subtask corresponds to the trace of the image patch from the tracked object. In order to improve the flexibility of multi-task tracker, the weighted nuclear norm is introduced to adaptively assign different tracking importance on different rank components of multiple tasks. The geometric structure relationship among and inside candidates (or training samples) are mined to learn the collaborate representation, according to the discriminative subspace and optimal classifier. They are simultaneously learned and updated by minimizing the developed tracking model. The best candidate is selected by jointly evaluating the normalized metric. The proposed tracker is empirically compared with the state-of-the-art trackers on a large set of public video sequences. Both quantitative and qualitative comparisons demonstrate that the proposed algorithm performs well in terms of effectiveness, accuracy and robustness.