{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:52:15Z","timestamp":1775609535450,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T00:00:00Z","timestamp":1664496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Laboratory of Resources and Environmental Information System"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pedestrian origin\u2013destination (O\u2013D) estimates that record traffic flows between origins and destinations, are essential for the management of pedestrian facilities including pedestrian flow simulation in the planning phase and crowd control in the operation phase. However, current O\u2013D data collection techniques such as surveys, mobile sensing using GPS, Wi-Fi, and Bluetooth, and smart card data have the disadvantage that they are either time consuming and costly, or cannot provide complete O\u2013D information for pedestrian facilities without entrances and exits or pedestrian flow inside the facilities. Due to the full coverage of CCTV cameras and the huge potential of image processing techniques, we address the challenges of pedestrian O\u2013D estimation and propose an image-based O\u2013D estimation framework. By identifying the same person in disjoint camera views, the O\u2013D trajectory of each identity can be accurately generated. Then, state-of-the-art deep neural networks (DNNs) for person re-ID at different congestion levels were compared and improved. Finally, an O\u2013D matrix based on trajectories was generated and the resident time was calculated, which provides recommendations for pedestrian facility improvement. The factors that affect the accuracy of the framework are discussed in this paper, which we believe could provide new insights and stimulate further research into the application of the Internet of cameras to intelligent transport infrastructure management.<\/jats:p>","DOI":"10.3390\/s22197429","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"7429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Pedestrian Origin\u2013Destination Estimation Based on Multi-Camera Person Re-Identification"],"prefix":"10.3390","volume":"22","author":[{"given":"Yan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC 3010, Australia"},{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Majid","family":"Sarvi","sequence":"additional","affiliation":[{"name":"Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6639-1727","authenticated-orcid":false,"given":"Kourosh","family":"Khoshelham","sequence":"additional","affiliation":[{"name":"Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Urban Planning and Landscape, North China University of Technology, Beijing 100144, China"},{"name":"Department of Urban Planning, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazhen","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.physleta.2018.10.029","article-title":"Simulating pedestrian flow through narrow exits","volume":"383","author":"Haghani","year":"2019","journal-title":"Phys. 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