{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:51:16Z","timestamp":1701478276943},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>In this paper, we propose a compact tracking framework built on top of Transformer. Our core design utilizes both attentional and convolutional operations and proposes a hybrid attentional module for simultaneous feature extraction and fusion of feature information between the target and the search image. This simultaneous modeling scheme allows the extraction of detailed features of the target and the fusion of features between the template and the search area. We use an attention mechanism with deep convolution to enhance the local attention to the target, as a way to ensure that the tracker achieves a balanced global and local attention for visual image target tracking. The experimental results show that the proposed method surpasses most of the mainstream trackers, and the operation speed achieves the real-time requirement with guaranteed accuracy.<\/jats:p>","DOI":"10.3233\/faia230927","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:58:58Z","timestamp":1701446338000},"source":"Crossref","is-referenced-by-count":0,"title":["Target Tracking Algorithm Based on Hybrid Attention Unification Framework"],"prefix":"10.3233","author":[{"given":"Xiaoyu","family":"Li","sequence":"first","affiliation":[{"name":"Chinese Flight Test Establishment, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Dai","sequence":"additional","affiliation":[{"name":"Chinese Flight Test Establishment, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Flight Test Establishment, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230927","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:59:03Z","timestamp":1701446343000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230927"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230927","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}