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This paper studies the visual tracking problem in video sequences and presents a novel robust sparse tracker under the particle filter framework. In particular, we propose an online robust non-negative dictionary learning algorithm for updating the object templates so that each learned template can capture a distinctive aspect of the tracked object. Another appealing property of this approach is that it can automatically detect and reject the occlusion and cluttered background in a principleddoi:10.1109/iccv.2013.87 dblp:conf/iccv/WangWY13 fatcat:kcrf5dsbvzh4fkftwy3e3jkz6i