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Appl."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>\n            Hyperspectral Object Tracking (HOT), utilizing rich spectral information from hyperspectral video (HSV), holds significant importance for object tracking. We identify that a major obstacle in improving HOT performance lies in effectively leveraging spectral and historical information. Furthermore, due to the mismatch in band dimensions between hyperspectral and RGB images, state-of-the-art RGB-based trackers struggle to adapt to unified HOT tasks. To address this, we propose a Historical Object-Aware Prompt Learning (HOPL) method for universal hyperspectral object tracking. Initially, we transform hyperspectral image (\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( N \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            bands) into multiple sets of three bands with different combinations and feed them into a backbone network to generate base features. Subsequently, we introduce a historical object-aware prompter, where historical object-aware images are input to generate prompt features that enhance the representation of object information when combined with base features. Additionally, we design a band information fusion module to integrate the multiple sets of base features. By introducing historical object-aware prompts, HOPL significantly enhances tracking performance without retraining the backbone network. Experimental results on the HOT2023 dataset (comprising HSV with 25-band, 16-band, and 15-band wavelength ranges) and HOT2022 dataset validate the superiority of HOPL over state-of-the-art methods. The source code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/rayyao\/HOPL\">https:\/\/github.com\/rayyao\/HOPL<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3736581","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T12:02:46Z","timestamp":1747828966000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Historical Object-Aware Prompt Learning for Universal Hyperspectral Object Tracking"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1289-3758","authenticated-orcid":false,"given":"Lu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China and Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2734-915X","authenticated-orcid":false,"given":"Rui","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China and Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2763-8782","authenticated-orcid":false,"given":"Yuhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou City, China, and Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6207-0299","authenticated-orcid":false,"given":"Yong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou City, China, and Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6818-2221","authenticated-orcid":false,"given":"Fuyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3564-5090","authenticated-orcid":false,"given":"Jiaqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou City, China, and Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9383-8384","authenticated-orcid":false,"given":"Zhiwen","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou City, China, and Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3557896"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3698399"},{"issue":"1","key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3699960","article-title":"Enhanced multi-object tracking: Inferring motion states of tracked objects","volume":"21","author":"Liao Pan","year":"2024","unstructured":"Pan Liao, Feng Yang, Di Wu, Bo Liu, Xingle Zhang, and Shangjun Zhou. 2024. 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