{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T12:10:41Z","timestamp":1774872641459,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"vor","delay-in-days":149,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014877","name":"Guangdong Ordinary University","doi-asserted-by":"publisher","award":["2020KTSCX399"],"award-info":[{"award-number":["2020KTSCX399"]}],"id":[{"id":"10.13039\/501100014877","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. Firstly, the motion region is extracted by the method of interframe difference and background difference. Then, the normalized motion region extracts the feature vectors based on the improved YOLOv3 tiny network. Finally, the trained linear support vector machine is used for pedestrian detection, and the performance of the fusion detection algorithm on caviar dataset is given, which proves the effectiveness of the proposed fusion detection algorithm. Experimental results show that the proposed method not only improves the practical application of pedestrian rerecognition but also reduces the detection range, computational complexity, and false detection rate compared with sliding window method.<\/jats:p>","DOI":"10.1155\/2021\/5596135","type":"journal-article","created":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T22:20:09Z","timestamp":1622413209000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3877-6288","authenticated-orcid":false,"given":"Meiman","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3860-8209","authenticated-orcid":false,"given":"Wenfu","family":"Xie","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,30]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"206","article-title":"Pedestrian detection method of texture feature and deep learning","volume":"35","author":"Zhang Y.","year":"2016","journal-title":"Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban)\/Journal of Liaoning Technical University (Natural Science Edition)"},{"key":"e_1_2_10_2_2","first-page":"2303","article-title":"Fast pedestrian detection based on motion information","volume":"11","author":"Yang Z.","year":"2015","journal-title":"Journal of Computational Information Systems"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.02.021"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmm.2016.2557729"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10249021"},{"key":"e_1_2_10_6_2","first-page":"4586","article-title":"Robust foreground detection using block-based RPCA","volume":"126","author":"Biao","year":"2015","journal-title":"Optik: Zeitschrift Fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.5954\/icarob.2020.os9-8"},{"key":"e_1_2_10_8_2","first-page":"279","article-title":"Pedestrian detection based on modified dynamic background using Gaussian mixture models and HOG-SVM detection","volume":"14","author":"Gui J. 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