UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES
In past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or lea...
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MDPI AG
2025-04-01
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| author | Shihai Cao Ting Wang Tao Li Shumin Fei |
| author_facet | Shihai Cao Ting Wang Tao Li Shumin Fei |
| author_sort | Shihai Cao |
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| description | In past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or leaves the view field, it may result in tracking loss of the target. To address these limitations, this work proposes an improved CF tracking algorithm based on some existent ones. Firstly, as for the scale changing of tracking target, an adaptive scale box is proposed to adjustably change the scale of the target box. Secondly, to address boundary effects caused by fast maneuvering, a spatio-temporal search strategy is presented, utilizing spatial context from the target region in the current frame and temporal information from preceding frames. Thirdly, aiming at the problem of tracking loss due to occlusion or out-of-view situations, this work proposes a fusion strategy based on the YOLOv5s_MSES target detection algorithm. Finally, the experimental results show that, compared to the baseline algorithm on the UAV123 dataset, our DP and AUC increased by 14.07% and 14.39%, respectively, and the frames per second (FPS) amounts to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>37.5</mn></mrow></semantics></math></inline-formula>. Additionally, on the OTB100 dataset, the proposed algorithm demonstrates significant improvements in distance precision (DP) metrics across four challenging attributes compared to the baseline algorithm, showing a 12.85% increase for scale variation (SV), 16.45% for fast motion (FM), 18.66% for occlusion (OCC), and 17.09% for out-of-view (OV) scenarios. To sum up, the proposed algorithm not only achieves the ideal tracking effect, but also meets the real-time requirement with higher precision, which means that the comprehensive performance is superior to some existing methods. |
| format | Article |
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| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Machines |
| spelling | doaj-art-f9d4fdf4f55e4187a2bc3cff2e0520c42025-08-20T03:47:58ZengMDPI AGMachines2075-17022025-04-0113536410.3390/machines13050364UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSESShihai Cao0Ting Wang1Tao Li2Shumin Fei3School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Automation, Southeast University, Nanjing 210096, ChinaIn past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or leaves the view field, it may result in tracking loss of the target. To address these limitations, this work proposes an improved CF tracking algorithm based on some existent ones. Firstly, as for the scale changing of tracking target, an adaptive scale box is proposed to adjustably change the scale of the target box. Secondly, to address boundary effects caused by fast maneuvering, a spatio-temporal search strategy is presented, utilizing spatial context from the target region in the current frame and temporal information from preceding frames. Thirdly, aiming at the problem of tracking loss due to occlusion or out-of-view situations, this work proposes a fusion strategy based on the YOLOv5s_MSES target detection algorithm. Finally, the experimental results show that, compared to the baseline algorithm on the UAV123 dataset, our DP and AUC increased by 14.07% and 14.39%, respectively, and the frames per second (FPS) amounts to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>37.5</mn></mrow></semantics></math></inline-formula>. Additionally, on the OTB100 dataset, the proposed algorithm demonstrates significant improvements in distance precision (DP) metrics across four challenging attributes compared to the baseline algorithm, showing a 12.85% increase for scale variation (SV), 16.45% for fast motion (FM), 18.66% for occlusion (OCC), and 17.09% for out-of-view (OV) scenarios. To sum up, the proposed algorithm not only achieves the ideal tracking effect, but also meets the real-time requirement with higher precision, which means that the comprehensive performance is superior to some existing methods.https://www.mdpi.com/2075-1702/13/5/364kernelized correlation filter (KCF)average peak correlation energy (APCE)target trackingYOLOv5s |
| spellingShingle | Shihai Cao Ting Wang Tao Li Shumin Fei UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES Machines kernelized correlation filter (KCF) average peak correlation energy (APCE) target tracking YOLOv5s |
| title | UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES |
| title_full | UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES |
| title_fullStr | UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES |
| title_full_unstemmed | UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES |
| title_short | UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES |
| title_sort | uav real time target detection and tracking algorithm based on improved kcf and yolov5s mses |
| topic | kernelized correlation filter (KCF) average peak correlation energy (APCE) target tracking YOLOv5s |
| url | https://www.mdpi.com/2075-1702/13/5/364 |
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