{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T06:13:55Z","timestamp":1762409635524,"version":"3.40.5"},"reference-count":33,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<p>Efficient tracking of heavy loads is the overall goal for overhead cranes in the workplace. This article presents a new way to simply and effectively track overhead cranes at higher speeds. A real-time tracking method is presented here to make tracking the overhead crane load not only accurate, but also faster. First, to observe and extract details and information to use in the appearance model, a measurement matrix is constructed here. Second, a sparse representation is also adopted to track dynamic overhead crane features effectively. Finally, a Na\u00efve Bayesian classifier has been used as a binary classification in a compressed domain. Experiments on the VOT2013 benchmark and constructed data Cranes40, the results demonstrate that the presented sparse representation tracking method can successfully track cranes in real-time.<\/p>","DOI":"10.4018\/ijaci.2019100103","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T14:06:38Z","timestamp":1568988398000},"page":"45-59","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Sparse Representation Based Visual Tracking Method for Dynamic Overhead Cranes"],"prefix":"10.4018","volume":"10","author":[{"given":"Tianlei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China & Department of Intelligent Manufacturing, Wuyi University, Jiangmen, China"}]},{"given":"Nanlin","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China"}]},{"given":"Ye","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Intelligent Manufacturing, Wuyi University, Jiangmen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0154-9743","authenticated-orcid":true,"given":"Yikui","family":"Zhai","sequence":"additional","affiliation":[{"name":"Department of Intelligent Manufacturing, Wuyi University, Jiangmen, China"}]}],"member":"2432","reference":[{"key":"IJACI.2019100103-0","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2009.2038395"},{"key":"IJACI.2019100103-1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2011.2163916"},{"key":"IJACI.2019100103-2","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2008.2008350"},{"key":"IJACI.2019100103-3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.468"},{"key":"IJACI.2019100103-4","doi-asserted-by":"crossref","first-page":"3883","DOI":"10.1109\/ICIP.2018.8451841","article-title":"Deep neural network based sparse measurement matrix for image compressed sensing.","author":"W.Cui","year":"2018","journal-title":"Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP)"},{"issue":"12","key":"IJACI.2019100103-5","first-page":"228","article-title":"Asymptotics of graphical projection pursuit.","author":"P.Diaconis","year":"1984","journal-title":"Annals of Statistics"},{"key":"IJACI.2019100103-6","doi-asserted-by":"publisher","DOI":"10.1109\/ICMECH.2007.4279993"},{"key":"IJACI.2019100103-7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.239"},{"key":"IJACI.2019100103-8","doi-asserted-by":"publisher","DOI":"10.1109\/ICNSC.2009.4919269"},{"key":"IJACI.2019100103-9","first-page":"191","article-title":"The Visual Object Tracking VOT2013 Challenge Results.","author":"M.Kristan","year":"2016","journal-title":"Proceedings of the ECCV 2016 Workshops on Computer Vision"},{"key":"IJACI.2019100103-10","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2013.2286565"},{"key":"IJACI.2019100103-11","doi-asserted-by":"crossref","unstructured":"Leitner, S., Wang, H., & Tragoudas, S. 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