{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:50:41Z","timestamp":1701478241973},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>The subsequent disquisition unveils an innovative tracking stratagem, specifically tailored for automotive monitoring situations. The recommended tactic aspires to perfect the blueprint, augmenting the detection briskness via a judicious synthesis of YOLOv4 and DeepSORT. The conventional convolution integral within YOLOv4 succumbs to depthwise separable convolution, culminating in a diminished computational exertion and accentuated recognition speed. In addition, the CSPNet framework nestled within YOLOv4 undergoes a tweaking process to pare down the parameter quantity of the blueprint. Alterations in the attribute extraction network of the Deep SORT algorithm facilitate effective re-identification, rendering it ideally malleable for automotive pursuit situations. Experimental excursions on custom datasets signify that despite a slight depreciation in average precision, the detection frame rate skyrockets to an impressive 132.4 FPS, thereby catering to tracking task prerequisites on gadgets with finite performance aptitude. As a result, this academic endeavor proffers a convenient resolution for real-time automotive tracking in settings operating under resource restrictions.<\/jats:p>","DOI":"10.3233\/faia230932","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:59:16Z","timestamp":1701446356000},"source":"Crossref","is-referenced-by-count":0,"title":["Optimized Real-Time Object Detection and Tracking Using a Refined YOLOV4 Algorithm"],"prefix":"10.3233","author":[{"given":"Haiwei","family":"Zuo","sequence":"first","affiliation":[{"name":"China Shenzhen Institute of Technology, No.1 Jiangjunmao Road, GuangDong, CHN 518000"}]},{"given":"Hehua","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Shenzhen Institute of Technology, No.1 Jiangjunmao Road, GuangDong, CHN 518000"}]},{"given":"Yanping","family":"Zhu","sequence":"additional","affiliation":[{"name":"China Shenzhen Institute of Technology, No.1 Jiangjunmao Road, GuangDong, CHN 518000"}]},{"given":"Yun","family":"Chen","sequence":"additional","affiliation":[{"name":"China Shenzhen Institute of Technology, No.1 Jiangjunmao Road, GuangDong, CHN 518000"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230932","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:59:23Z","timestamp":1701446363000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230932"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230932","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}