{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T16:12:40Z","timestamp":1780503160994,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP19H04189"],"award-info":[{"award-number":["JP19H04189"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a proximity imaging sensor based on a tomographic approach with a low-cost conductive sheet. Particularly, by defining capacitance density, physical proximity information is transformed into electric potential. A novel theoretical model is developed to solve the capacitance density problem using the tomographic approach. Additionally, a prototype is built and tested based on the model, and the system solves an inverse problem for imaging the capacitance density change that indicates the object\u2019s proximity change. In the evaluation test, the prototype reaches an error rate of 10.0\u201315.8% in horizontal localization at different heights. Finally, a hand-tracking demonstration is carried out, where a position difference of 33.8\u201346.7 mm between the proposed sensor and depth camera is achieved at 30 fps.<\/jats:p>","DOI":"10.3390\/s21082736","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T12:34:50Z","timestamp":1618317290000},"page":"2736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Tomographic Proximity Imaging Using Conductive Sheet for Object Tracking"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1416-2698","authenticated-orcid":false,"given":"Zehao","family":"Li","sequence":"first","affiliation":[{"name":"School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8583-6699","authenticated-orcid":false,"given":"Shunsuke","family":"Yoshimoto","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8563, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akio","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8563, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.neucom.2019.11.023","article-title":"Deep learning in video multi-object tracking: A survey","volume":"381","author":"Ciaparrone","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.3233\/JIFS-152381","article-title":"A novel object tracking algorithm by fusing color and depth information based on single valued neutrosophic cross-entropy","volume":"32","author":"Hu","year":"2017","journal-title":"J. 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