{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:25:19Z","timestamp":1759537519388,"version":"build-2065373602"},"reference-count":26,"publisher":"World Scientific Pub Co Pte Ltd","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p> With the development of video surveillance and autonomous driving technology, target tracking in dynamic scenes has become an important research direction in the field of computer vision. YOLOv9, as the latest object detection algorithm, performs well in real-time object detection with its high speed and accuracy. The core of this study is to combine nonlinear filtering technology with a YOLOv9 detector to achieve accurate tracking of targets in dynamic scenes. The experimental results show that as the number of iterations increases, the accuracy of most tracking algorithms shows an overall upward trend, but the performance differences between different algorithms are significant. N-YOLOv9 exhibits high accuracy at multiple iteration points, especially at high iteration times, where its performance advantage is more pronounced. In contrast, the accuracy of EMTT-YOLO, WBi YOLOSF, SCAN and Violence YOLO is relatively low and fluctuates greatly, indicating that these algorithms may have certain limitations in handling complex tracking tasks. The target tracking method based on nonlinear filtering and YOLOv9 proposed in this paper not only improves the accuracy and robustness of tracking but also maintains real-time processing capability. Future work will focus on further optimizing algorithm performance and exploring its applicability in a wider range of application scenarios. <\/jats:p>","DOI":"10.1142\/s0218126625502032","type":"journal-article","created":{"date-parts":[[2025,1,18]],"date-time":"2025-01-18T00:16:29Z","timestamp":1737159389000},"source":"Crossref","is-referenced-by-count":0,"title":["Nonlinear Filtering and YOLOv9-Based Moving Object Tracking Method Under Dynamic Scenarios"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5987-462X","authenticated-orcid":false,"given":"Tingting","family":"Wu","sequence":"first","affiliation":[{"name":"Xihua University, Chengdu 610039, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingfeng","family":"Fan","sequence":"additional","affiliation":[{"name":"China University of Political Science and Law, Beijing 100091, P. R. 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