{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T18:40:46Z","timestamp":1789065646240,"version":"build-2803163510"},"reference-count":47,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp; D Program of China","award":["No. 2017YFC0803905"],"award-info":[{"award-number":["No. 2017YFC0803905"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["No. 2020A1515111001"],"award-info":[{"award-number":["No. 2020A1515111001"]}]},{"name":"Ordinary University Engineering Technology Development Center Project of Guangdong Province","award":["No. 2019GCZX006"],"award-info":[{"award-number":["No. 2019GCZX006"]}]},{"name":"Guangdong Provincial Ordinary University Characteristics and Innovation Project","award":["No. 2019KTSCX205"],"award-info":[{"award-number":["No. 2019KTSCX205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The advantages of UAV video in flexibility, traceability, easy-operation, and abundant information make it a popular and powerful aerial tool applied in traffic monitoring in recent years. This paper proposed a systematic approach to detect and track vehicles based on the YOLO v3 model and the deep SORT algorithm for further extracting key traffic parameters. A field experiment was implemented to provide data for model training and validation to ensure the accuracy of the proposed approach. In the experiment, 5400 frame images and 1192 speed points were collected from two test vehicles equipped with high-precision GNSS-RTK and onboard OBD after completion of seven experimental groups with a different height (150 m to 500 m) and operating speed (40 km\/h to 90 km\/h). The results indicate that the proposed approach exhibits strong robustness and reliability, due to the 90.88% accuracy of object detection and 98.9% precision of tracking vehicle. Moreover, the absolute and relative error of extracted speed falls within \u00b13 km\/h and 2%, respectively. The overall accuracy of the extracted parameters reaches up to 98%.<\/jats:p>","DOI":"10.3390\/s21165620","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T22:59:27Z","timestamp":1629673167000},"page":"5620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Extracting Key Traffic Parameters from UAV Video with On-Board Vehicle Data Validation"],"prefix":"10.3390","volume":"21","author":[{"given":"Donghui","family":"Shan","sequence":"first","affiliation":[{"name":"Research Center of Traffic Safety and Emergency Security Technology, CCCC First Highway Consultants Co., Ltd., Xi\u2019an 715000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4856-5161","authenticated-orcid":false,"given":"Tian","family":"Lei","sequence":"additional","affiliation":[{"name":"Guangdong Rail Transit Intelligent Operation and Maintenance Technology Development Center, College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Yin","sequence":"additional","affiliation":[{"name":"Guangdong Rail Transit Intelligent Operation and Maintenance Technology Development Center, College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Luo","sequence":"additional","affiliation":[{"name":"Guangdong Rail Transit Intelligent Operation and Maintenance Technology Development Center, College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Gong","sequence":"additional","affiliation":[{"name":"Guangdong Rail Transit Intelligent Operation and Maintenance Technology Development Center, College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/TITS.2006.888597","article-title":"Vehicle and guard rail detection using radar and vision data fusion","volume":"8","author":"Alessandretti","year":"2007","journal-title":"IEEE Trans. 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