{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:11:04Z","timestamp":1753884664112,"version":"3.41.2"},"reference-count":37,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","funder":[{"name":"the Funding Project of Humanities and Social Sciences of the Ministry of Education in China","award":["22YJAZH002"],"award-info":[{"award-number":["22YJAZH002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p>Social distance monitoring is of great significance for public health in the era of COVID-19 pandemic. However, existing monitoring methods cannot effectively detect social distance in terms of efficiency, accuracy, and robustness. In this paper, we proposed a social distance monitoring method based on an improved YOLOv4 algorithm. Specifically, our method constructs and pre-processes a dataset. Afterwards, our method screens the valid samples and improves the K-means clustering algorithm based on the IoU distance. Then, our method detects the target pedestrians using a trained improved YOLOv4 algorithm and gets the pedestrian target detection frame location information. Finally, our method defines the observation depth parameters, generates the 3D feature space, and clusters the offending aggregation groups based on the L2 parametric distance to finally realize the pedestrian social distance monitoring of 2D video. Experiments show that the proposed social distance monitoring method based on improved YOLOv4 can accurately detect pedestrian target locations in video images, where the pre-processing operation and improved K-means algorithm can improve the pedestrian target detection accuracy. Our method can cluster the offending groups without going through calibration mapping transformation to realize the pedestrian social distance monitoring of 2D videos.<\/jats:p>","DOI":"10.1142\/s0218001423540071","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T01:39:39Z","timestamp":1675993179000},"source":"Crossref","is-referenced-by-count":2,"title":["A Social Distance Monitoring Method Based on Improved YOLOv4 for Surveillance Videos"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4885-3312","authenticated-orcid":false,"given":"Xingquan","family":"Cai","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P. R. China"}]},{"given":"Shun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P. R. China"}]},{"given":"Pengyan","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P. R. China"}]},{"given":"Dingwei","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P. R. China"}]},{"given":"Haiyan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P. R. China"}]},{"given":"Jiaqi","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Hebei Normal University for Nationalities, Chengde 067000, P. R. China"},{"name":"The Technology Innovation Center of Cultural Tourism, Big Data of Hebei Province, Chengde 067000, P. R. 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