{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T14:37:59Z","timestamp":1775054279355,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2018,5,18]],"date-time":"2018-05-18T00:00:00Z","timestamp":1526601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61605205"],"award-info":[{"award-number":["61605205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2019,6,25]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Correlation filter (CF)\u2013based tracking algorithms is most popular in recent years due to its high accuracy and impressive speed. However, it has some intrinsically drawbacks such as margin suppression, sensitive for disturbance, and partial occlusions. Contrasted with CF drawbacks, the advantages of particle filter (PF) tracking algorithm include robustness, motion prediction, and wide detection range. Therefore, it can amend some CF tracker drawbacks. On the other hand, the HOG feature is widely used in CF tracker because it can detect the target precision position. However, this kind of feature is rotation\u2010variation, which is invalid for rotation transformation target. On the contrary, the tracker precision merely based on colour feature is rough, but colour feature is rotation invariation and is effective for rotating target; therefore, these two features are complementary. In this paper, we integrate both trackers (CF and PF) to learn the HOG and colour feature, respectively, experiments demonstrate this tracking algorithm is more robust, and the tracking precision is more accurate. This algorithm is integrated with some classic CF trackers (KCF, SAMF, and MOSSE) framework and benchmark them against their baseline. On the OTB2015 benchmark datasets, experiment result demonstrates OPE performance grades have improved from about 1% to 12%; SRE Performance grades have improved from about 1.3% to 5.8%.<\/jats:p>","DOI":"10.1002\/cpe.4665","type":"journal-article","created":{"date-parts":[[2018,5,18]],"date-time":"2018-05-18T08:38:49Z","timestamp":1526632729000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Collaborating visual tracker based on particle filter and correlation filter"],"prefix":"10.1002","volume":"31","author":[{"given":"Weiguang","family":"Li","sequence":"first","affiliation":[{"name":"Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China"},{"name":"Artificial Intelligence Key Laboratory Sichuan University of Science and Engineering Zigong 643000 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Wei","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences Beijing 100049 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Qiang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Key Laboratory Sichuan University of Science and Engineering Zigong 643000 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-7858-1878","authenticated-orcid":false,"given":"Mingquan","family":"Shi","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,5,18]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"KristanM MatasJ LeonardisA FelsbergM.The visual object tracking VOT2015 challenge results. 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