{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:51:41Z","timestamp":1701478301986},"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>Local occlusion may be developed during the target motion, such that it is urgent to solve the problem of video tracking loss caused by moving target occlusion. In this paper, the computer vision library OpenCV is used to preprocess the motion video frame. Two algorithms are combined to solve the problem of tracking loss due to target-background similarity and occlusion: one is the Camshift algorithm (which is used to track the moving target); the other is the Kalman filter (which is used to predict the target position). Comparing with Meanshift algorithms in the same experimental environment, the results show that the proposed Camshift-based method can effectively filter out the background interference in the tracking process based on accurate detection and tracking of moving targets, and make the motion information of occluded targets more prominent. Good performance has been achieved in the interference and occlusion environment, and it has certain real-time performance and effectiveness.<\/jats:p>","DOI":"10.3233\/faia230837","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:54:23Z","timestamp":1701446063000},"source":"Crossref","is-referenced-by-count":0,"title":["Moving Object Detection and Tracking in Video Frame Based on OpenCV"],"prefix":"10.3233","author":[{"given":"Yueting","family":"Yang","sequence":"first","affiliation":[{"name":"Jilin University of Chemical Technology, China"},{"name":"Guangdong University of Petrochemical Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaolin","family":"Hu","sequence":"additional","affiliation":[{"name":"Guangdong University of Petrochemical Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guogang","family":"Wang","sequence":"additional","affiliation":[{"name":"Jilin University of Chemical Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Ke","sequence":"additional","affiliation":[{"name":"Guangdong University of Petrochemical Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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\/FAIA230837","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:54:30Z","timestamp":1701446070000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230837","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]]}}}