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For this purpose, the Huber loss function and the Fisher weight coefficient is used in the discriminative term to improve computational efficiency. In addition, non-negative constraints is added on dictionaries to enhance the performance. The OTB50 and OTB100 datasets are used to evaluate our tracker and compare with related algorithm. The experimental results show that our method performs much better than the tracking method compared in this paper.<\/jats:p>","DOI":"10.1186\/s13634-019-0638-0","type":"journal-article","created":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T20:28:08Z","timestamp":1572467288000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Online non-negative discriminative dictionary learning for tracking"],"prefix":"10.1186","volume":"2019","author":[{"given":"Weisong","family":"Wang","sequence":"first","affiliation":[]},{"given":"Fei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Hongzhi","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,30]]},"reference":[{"issue":"4","key":"638_CR1","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.1109\/TASE.2018.2877499","volume":"16","author":"Wei Zhang","year":"2019","unstructured":"W. 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