{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T18:33:39Z","timestamp":1757702019571,"version":"3.41.0"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,10,10]],"date-time":"2018-10-10T00:00:00Z","timestamp":1539129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-1330491"],"award-info":[{"award-number":["CNS-1330491"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2018,11,30]]},"abstract":"<jats:p>\n            Currently, the surveillance camera-based person re-identification is still challenging because of diverse factors such as people\u2019s changing poses and various illumination. The various poses make it hard to conduct feature matching across images, and the illumination changes make color-based features unreliable. In this article, we present SKEPRID,\n            <jats:sup>1<\/jats:sup>\n            a skeleton-based person re-identification method that handles strong pose and illumination changes jointly. To reduce the impacts of pose changes on re-identification, we estimate the joints\u2019 positions of a person based on the deep learning technique and thus make it possible to extract features on specific body parts with high accuracy. Based on the skeleton information, we design a set of local color comparison-based cloth-type features, which are resistant to various lighting conditions. Moreover, to better evaluate SKEPRID, we build the PO8LI\n            <jats:sup>2<\/jats:sup>\n            dataset, which has large pose and illumination diversity. Our experimental results show that SKEPRID outperforms state-of-the-art approaches in the case of strong pose and illumination variation.\n          <\/jats:p>","DOI":"10.1145\/3243217","type":"journal-article","created":{"date-parts":[[2018,10,10]],"date-time":"2018-10-10T13:30:46Z","timestamp":1539178246000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["SKEPRID"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5209-6265","authenticated-orcid":false,"given":"Tuo","family":"Yu","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiming","family":"Jin","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wai-Tian","family":"Tan","sequence":"additional","affiliation":[{"name":"Cisco Systems, Inc., CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klara","family":"Nahrstedt","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,10,10]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"1","article-title":"Sports videos in the wild (SVW): A video dataset for sports analysis","volume":"1","author":"Safdarnejad S. 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