{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:08:19Z","timestamp":1775066899885,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MHLW Health, Labour, and Welfare Sciences Research Grants Research on Medical ICT and Artificial Intelligence Program","award":["20AC1004"],"award-info":[{"award-number":["20AC1004"]}]},{"name":"MHLW Health, Labour, and Welfare Sciences Research Grants Research on Medical ICT and Artificial Intelligence Program","award":["201603003"],"award-info":[{"award-number":["201603003"]}]},{"name":"MHLW Health, Labour, and Welfare Sciences Research Grants Research on Medical ICT and Artificial Intelligence Program","award":["22H03617"],"award-info":[{"award-number":["22H03617"]}]},{"name":"MIC\/SCOPE","award":["20AC1004"],"award-info":[{"award-number":["20AC1004"]}]},{"name":"MIC\/SCOPE","award":["201603003"],"award-info":[{"award-number":["201603003"]}]},{"name":"MIC\/SCOPE","award":["22H03617"],"award-info":[{"award-number":["22H03617"]}]},{"name":"JSPS KAKENHI","award":["20AC1004"],"award-info":[{"award-number":["20AC1004"]}]},{"name":"JSPS KAKENHI","award":["201603003"],"award-info":[{"award-number":["201603003"]}]},{"name":"JSPS KAKENHI","award":["22H03617"],"award-info":[{"award-number":["22H03617"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Multi-camera multi-person (MCMP) tracking and re-identification (ReID) are essential tasks in safety, pedestrian analysis, and so on; however, most research focuses on outdoor scenarios because they are much more complicated to deal with occlusions and misidentification in a crowded room with obstacles. Moreover, it is challenging to complete the two tasks in one framework. We present a trajectory-based method, integrating tracking and ReID tasks. First, the poses of all surgical members captured by each camera are detected frame-by-frame; then, the detected poses are exploited to track the trajectories of all members for each camera; finally, these trajectories of different cameras are clustered to re-identify the members in the operating room across all cameras. Compared to other MCMP tracking and ReID methods, the proposed one mainly exploits trajectories, taking texture features that are less distinguishable in the operating room scenario as auxiliary cues. We also integrate temporal information during ReID, which is more reliable than the state-of-the-art framework where ReID is conducted frame-by-frame. In addition, our framework requires no training before deployment in new scenarios. We also created an annotated MCMP dataset with actual operating room videos. Our experiments prove the effectiveness of the proposed trajectory-based ReID algorithm. The proposed framework achieves 85.44% accuracy in the ReID task, outperforming the state-of-the-art framework in our operating room dataset.<\/jats:p>","DOI":"10.3390\/jimaging8080219","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T21:23:56Z","timestamp":1660771436000},"page":"219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-Camera Multi-Person Tracking and Re-Identification in an Operating Room"],"prefix":"10.3390","volume":"8","author":[{"given":"Haowen","family":"Hu","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Tokyo 223-8522, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8274-3710","authenticated-orcid":false,"given":"Ryo","family":"Hachiuma","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Tokyo 223-8522, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-9862","authenticated-orcid":false,"given":"Hideo","family":"Saito","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Tokyo 223-8522, Japan"}]},{"given":"Yoshifumi","family":"Takatsume","sequence":"additional","affiliation":[{"name":"Department of Anatomy, Keio University School of Medicine, Tokyo 160-8582, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3267-5884","authenticated-orcid":false,"given":"Hiroki","family":"Kajita","sequence":"additional","affiliation":[{"name":"Department of Plastic and Reconstructive Surgery, Keio University School of Medicine, Tokyo 160-8582, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4924","DOI":"10.1007\/s00464-019-07281-0","article-title":"Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach","volume":"34","author":"Kitaguchi","year":"2020","journal-title":"Surg. 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