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These variations often result in model drift and tracking failure, particularly in complex and dynamic environments. To address these challenges, this study proposes an adaptive online tracking algorithm that fuses hierarchical convolutional features through adaptive weighting and employs a dynamic model\u2010updating mechanism. In the proposed approach, correlation filters are independently trained on each convolutional layer to generate multilevel response maps. These maps are then adaptively fused using learned weights to produce a final response map, from which the target position is determined based on the peak response. Additionally, a dynamic update strategy is designed to mitigate the degradation of the appearance model during long\u2010term tracking. Extensive experiments conducted on several public benchmark datasets demonstrate that the proposed tracker achieves superior robustness and accuracy compared to many state\u2010of\u2010the\u2010art methods, confirming its effectiveness for practical visual tracking applications.<\/jats:p>","DOI":"10.1155\/acis\/7770863","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T09:31:43Z","timestamp":1780651903000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Multilevel Feature Fusion and Dynamic Model Updating for Robust Visual Object Tracking"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0773-4897","authenticated-orcid":false,"given":"Xianyou","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6693-0161","authenticated-orcid":false,"given":"Hengyou","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3458-9493","authenticated-orcid":false,"given":"Chengzhen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9461-0691","authenticated-orcid":false,"given":"Xingyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"65","article-title":"Chapter 5-An Efficient Framework for Object Tracking in Video Surveillance","author":"Mohanapriya D.","year":"2020","journal-title":"The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-01848-y"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-00223-x"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/mspec.2016.7419800"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-017-0878-7"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2008.299"},{"key":"e_1_2_10_7_2","unstructured":"DalalN.andTriggsB. 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