Robust UAV Target Tracking Algorithm Based on Saliency Detection
Due to their high efficiency and real-time performance, discriminant correlation filtering (DCF) trackers have been widely applied in unmanned aerial vehicle (UAV) tracking. However, the robustness of existing trackers is still poor when facing complex scenes, such as background clutter, occlusion,...
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MDPI AG
2025-04-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/4/285 |
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| author | Hanqing Wu Weihua Wang Gao Chen Xin Li |
| author_facet | Hanqing Wu Weihua Wang Gao Chen Xin Li |
| author_sort | Hanqing Wu |
| collection | DOAJ |
| description | Due to their high efficiency and real-time performance, discriminant correlation filtering (DCF) trackers have been widely applied in unmanned aerial vehicle (UAV) tracking. However, the robustness of existing trackers is still poor when facing complex scenes, such as background clutter, occlusion, camera motion, and scale variations. In response to this problem, this paper proposes a robust UAV target tracking algorithm based on saliency detection (SDBCF). Using saliency detection methods, the DCF tracker is optimized in three aspects to enhance the robustness of the tracker in complex scenes: feature fusion, filter-model construct, and scale-estimation methods improve. Firstly, this article analyzes the features from both spatial and temporal dimensions, evaluates the representational and discriminative abilities of different features, and achieves adaptive feature fusion. Secondly, this paper constructs a dynamic spatial regularization term using a mask that fits the target, and integrates it with a second-order differential regularization term into the DCF framework to construct a novel filter model, which is solved using the ADMM method. Next, this article uses saliency detection to supervise the aspect ratio of the target, and trains a scale filter in the continuous domain to improve the tracker’s adaptability to scale variations. Finally, comparative experiments were conducted with various DCF trackers on three UAV datasets: UAV123, UAV20L, and DTB70. The DP and AUC scores of SDBCF on the three datasets were (71.5%, 58.9%), (63.0%, 57.8%), and (72.1%, 48.4%), respectively. The experimental results indicate that SDBCF achieves a superior performance. |
| format | Article |
| id | doaj-art-bfcbccb2c3034029b48e5d5020f4d40e |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Drones |
| spelling | doaj-art-bfcbccb2c3034029b48e5d5020f4d40e2025-08-20T02:28:14ZengMDPI AGDrones2504-446X2025-04-019428510.3390/drones9040285Robust UAV Target Tracking Algorithm Based on Saliency DetectionHanqing Wu0Weihua Wang1Gao Chen2Xin Li3National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaDue to their high efficiency and real-time performance, discriminant correlation filtering (DCF) trackers have been widely applied in unmanned aerial vehicle (UAV) tracking. However, the robustness of existing trackers is still poor when facing complex scenes, such as background clutter, occlusion, camera motion, and scale variations. In response to this problem, this paper proposes a robust UAV target tracking algorithm based on saliency detection (SDBCF). Using saliency detection methods, the DCF tracker is optimized in three aspects to enhance the robustness of the tracker in complex scenes: feature fusion, filter-model construct, and scale-estimation methods improve. Firstly, this article analyzes the features from both spatial and temporal dimensions, evaluates the representational and discriminative abilities of different features, and achieves adaptive feature fusion. Secondly, this paper constructs a dynamic spatial regularization term using a mask that fits the target, and integrates it with a second-order differential regularization term into the DCF framework to construct a novel filter model, which is solved using the ADMM method. Next, this article uses saliency detection to supervise the aspect ratio of the target, and trains a scale filter in the continuous domain to improve the tracker’s adaptability to scale variations. Finally, comparative experiments were conducted with various DCF trackers on three UAV datasets: UAV123, UAV20L, and DTB70. The DP and AUC scores of SDBCF on the three datasets were (71.5%, 58.9%), (63.0%, 57.8%), and (72.1%, 48.4%), respectively. The experimental results indicate that SDBCF achieves a superior performance.https://www.mdpi.com/2504-446X/9/4/285UAV target trackingcorrelation filteringfeature fusiondynamic spatial regularization termscale estimation |
| spellingShingle | Hanqing Wu Weihua Wang Gao Chen Xin Li Robust UAV Target Tracking Algorithm Based on Saliency Detection Drones UAV target tracking correlation filtering feature fusion dynamic spatial regularization term scale estimation |
| title | Robust UAV Target Tracking Algorithm Based on Saliency Detection |
| title_full | Robust UAV Target Tracking Algorithm Based on Saliency Detection |
| title_fullStr | Robust UAV Target Tracking Algorithm Based on Saliency Detection |
| title_full_unstemmed | Robust UAV Target Tracking Algorithm Based on Saliency Detection |
| title_short | Robust UAV Target Tracking Algorithm Based on Saliency Detection |
| title_sort | robust uav target tracking algorithm based on saliency detection |
| topic | UAV target tracking correlation filtering feature fusion dynamic spatial regularization term scale estimation |
| url | https://www.mdpi.com/2504-446X/9/4/285 |
| work_keys_str_mv | AT hanqingwu robustuavtargettrackingalgorithmbasedonsaliencydetection AT weihuawang robustuavtargettrackingalgorithmbasedonsaliencydetection AT gaochen robustuavtargettrackingalgorithmbasedonsaliencydetection AT xinli robustuavtargettrackingalgorithmbasedonsaliencydetection |