{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:42:21Z","timestamp":1770918141216,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52175279"],"award-info":[{"award-number":["52175279"]}]},{"name":"National Natural Science Foundation of China","award":["51705459"],"award-info":[{"award-number":["51705459"]}]},{"name":"National Natural Science Foundation of China","award":["LY20E050022"],"award-info":[{"award-number":["LY20E050022"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["52175279"],"award-info":[{"award-number":["52175279"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["51705459"],"award-info":[{"award-number":["51705459"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["LY20E050022"],"award-info":[{"award-number":["LY20E050022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The tracking of a particular pedestrian is an important issue in computer vision to guarantee societal safety. Due to the limited computing performances of unmanned aerial vehicle (UAV) systems, the Correlation Filter (CF) algorithm has been widely used to perform the task of tracking. However, it has a fixed template size and cannot effectively solve the occlusion problem. Thus, a tracking-by-detection framework was designed in the current research. A lightweight YOLOv3-based (You Only Look Once version 3) mode which had Efficient Channel Attention (ECA) was integrated into the CF algorithm to provide deep features. In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. As a result, a Deep Feature Kernelized Correlation Filters method coupled with Siamese-CSP(Siam-DFKCF) was established to increase the tracking robustness. From the experimental results, it can be concluded that the anti-occlusion and re-tracking performance of the proposed method was increased. The tracking accuracy Distance Precision (DP) and Overlap Precision (OP) had been increased to 0.934 and 0.909 respectively in our test data.<\/jats:p>","DOI":"10.3390\/s23010482","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T04:56:27Z","timestamp":1672635387000},"page":"482","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4770-4788","authenticated-orcid":false,"given":"Di","family":"Tang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijie","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawei","family":"Liu","sequence":"additional","affiliation":[{"name":"China Aerodynamics Research and Development Center, High Speed Aerodynamic Institute, Mianyang 621000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingqi","family":"Che","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Yang","sequence":"additional","affiliation":[{"name":"China Aerodynamics Research and Development Center, High Speed Aerodynamic Institute, Mianyang 621000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1080\/09546553.2018.1439023","article-title":"Research on terrorism, 2007\u20132016: A review of data, methods, and authorship","volume":"32","author":"Schuurman","year":"2020","journal-title":"Terror. 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