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A kernelized correlation filter with an adaptive update scheme is adopted to estimate target position. The adaptive online update scheme takes advantage of the confidence score sensitivity to occlusion and reduces the false updating in occlusion during the tracking sequence. The target scale can be estimated by the correlation filter with the ridge regression. Extensive experiments results on 29 challenging occlusion sequences show that the proposed tracking approach achieves the average overlap precision (OP) of 72.2%, which improves the performance by 7.6% compared to the DSST. On OTB-50 dataset, our tracking approach is also superior comparing to several state-of-the-art trackers.<\/jats:p>","DOI":"10.3233\/jifs-171071","type":"journal-article","created":{"date-parts":[[2018,5,1]],"date-time":"2018-05-01T10:18:31Z","timestamp":1525169911000},"page":"3983-3991","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust object tracking with scene-adaptive scheme in occlusion"],"prefix":"10.1177","volume":"34","author":[{"given":"Zhiyong","family":"an","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing in Universities of Shandong (Shandong Institute of Business and Technology), Yantai, China"},{"name":"Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China"}]},{"given":"Guan","family":"hao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Yuan","family":"li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan, China"}]}],"member":"179","published-online":{"date-parts":[[2018,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","article-title":"Incremental learning for robust visual tracking","volume":"1","author":"Ross D.","year":"2008","unstructured":"RossD., LimJ., LinR.S. and YangM.H., Incremental learning for robust visual tracking, International Journal of Computer Vision 1 (2008), 125\u2013141.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_3_2_3_2","first-page":"1269","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press","author":"Kwon J.","year":"2010","unstructured":"KwonJ., LeeK.M. 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