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The local hue histograms are used to model the target, and the circle matching criterion is used to locate the target, which can restrain the disturbance from the adjacent regions in the background. It can also adjust adaptively the size of the tracking target according to the local matching results. Therefore, the tracking method based on the cognitive associative network has good invariability to the scale, the rotation, and the partial occlusion. Simulation results show that the tracking method can perform the target tracking in the disturbance environment or in the scene of the complex motion. The tracking method can locate the target more accurately than the common tracking methods such as the Camshift method or the compressive tracking method.<\/jats:p>","DOI":"10.1049\/iet-ipr.2018.5461","type":"journal-article","created":{"date-parts":[[2018,11,26]],"date-time":"2018-11-26T21:17:01Z","timestamp":1543267021000},"page":"498-505","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Target tracking based on the cognitive associative network"],"prefix":"10.1049","volume":"13","author":[{"given":"Chunbo","family":"Xiu","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin polytechnic University Tianjin 300387 People's Republic of China"},{"name":"Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin polytechnic University Tianjin 300387 People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuohong","family":"Chai","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin polytechnic University Tianjin 300387 People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2019,1,25]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2013.10.002"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.optcom.2015.11.077"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.02.024"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2017.05.022"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.02.071"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2014.0226"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2014.0803"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1251157"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2016.01.116"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2015.11.008"},{"issue":"5","key":"e_1_2_6_12_1","first-page":"704","article-title":"An adaptive pedestrian tracking algorithm with prior knowledge","volume":"22","author":"Cheng Y.L.","year":"2009","journal-title":"Pattern Recognit. 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