{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:28:17Z","timestamp":1740148097363,"version":"3.37.3"},"reference-count":14,"publisher":"Wiley","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Astronautic Science and Technology Innovation Foundation","award":["CASC201104","2012ZC53043"],"award-info":[{"award-number":["CASC201104","2012ZC53043"]}]},{"name":"Aviation Science Fund","award":["CASC201104","2012ZC53043"],"award-info":[{"award-number":["CASC201104","2012ZC53043"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.<\/jats:p>","DOI":"10.1155\/2016\/5894639","type":"journal-article","created":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T02:09:26Z","timestamp":1471572566000},"page":"1-13","source":"Crossref","is-referenced-by-count":0,"title":["Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking"],"prefix":"10.1155","volume":"2016","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4124-5317","authenticated-orcid":true,"given":"Honghong","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Automation, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiru","family":"Qu","sequence":"additional","affiliation":[{"name":"Department of Automation, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1145\/1177352.1177355"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2008.146"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-007-0075-7"},{"key":"7","first-page":"1","volume":"26","year":"2015","journal-title":"IEEE Transactions on Software Engineering"},{"issue":"10","key":"8","first-page":"1348","volume":"27","year":"2007","journal-title":"IEEE Transactions on Circuits & Systems for Video Technology"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-006-5568-2"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.70727"},{"issue":"11","key":"12","first-page":"2259","volume":"33","year":"2011","journal-title":"IEEE Transactions on Software Engineering"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2011.12.004"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33783-3_34"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2255301"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_45"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.827145"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/5894639.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/5894639.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/5894639.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T02:09:28Z","timestamp":1471572568000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/cin\/2016\/5894639\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":14,"alternative-id":["5894639","5894639"],"URL":"https:\/\/doi.org\/10.1155\/2016\/5894639","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2016]]}}}