UAV object tracking algorithm based on spatial saliency-aware correlation filter
Recently, correlation filter-based tracking methods have been widely adopted in UAV target tracking due to their outstanding performance and excellent tracking efficiency. However, existing correlation filter-based tracking methods still face issues such as redundant visual features with weak discri...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-03-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2025068 |
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| author | Changhui Wu Jinrong Shen Kaiwei Chen Yingpin Chen Yuan Liao |
| author_facet | Changhui Wu Jinrong Shen Kaiwei Chen Yingpin Chen Yuan Liao |
| author_sort | Changhui Wu |
| collection | DOAJ |
| description | Recently, correlation filter-based tracking methods have been widely adopted in UAV target tracking due to their outstanding performance and excellent tracking efficiency. However, existing correlation filter-based tracking methods still face issues such as redundant visual features with weak discriminative ability, inadequate spatio-temporal information mining, and filter degradation. In order to overcome these challenges, this paper proposes a spatial saliency-aware strategy that reduces redundant information in spatial and channel dimensions, thus improving the discriminative ability between the target and background. Also, this paper proposes a position estimation mechanism under spatio-temporal joint constraints to fully mine spatio-temporal information and enhance the robustness of the model in complex scenarios. Furthermore, this paper establishes a positive expert group using historical positive samples to assess the reliability of candidate samples, thereby effectively mitigating the filter degradation issue. Ultimately, the effectiveness of the proposed method is demonstrated through the evaluation of multiple public datasets. The experimental results reveal that this method outperforms others in tracking performance under various challenging conditions. |
| format | Article |
| id | doaj-art-deddb224a90b4b218a5ddb087e042536 |
| institution | OA Journals |
| issn | 2688-1594 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-deddb224a90b4b218a5ddb087e0425362025-08-20T02:08:24ZengAIMS PressElectronic Research Archive2688-15942025-03-013331446147510.3934/era.2025068UAV object tracking algorithm based on spatial saliency-aware correlation filterChanghui Wu0Jinrong Shen1Kaiwei Chen2Yingpin Chen3Yuan Liao4School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaSchool of Computer Science, Minnan Normal University, Zhangzhou, ChinaSchool of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaSchool of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaSchool of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaRecently, correlation filter-based tracking methods have been widely adopted in UAV target tracking due to their outstanding performance and excellent tracking efficiency. However, existing correlation filter-based tracking methods still face issues such as redundant visual features with weak discriminative ability, inadequate spatio-temporal information mining, and filter degradation. In order to overcome these challenges, this paper proposes a spatial saliency-aware strategy that reduces redundant information in spatial and channel dimensions, thus improving the discriminative ability between the target and background. Also, this paper proposes a position estimation mechanism under spatio-temporal joint constraints to fully mine spatio-temporal information and enhance the robustness of the model in complex scenarios. Furthermore, this paper establishes a positive expert group using historical positive samples to assess the reliability of candidate samples, thereby effectively mitigating the filter degradation issue. Ultimately, the effectiveness of the proposed method is demonstrated through the evaluation of multiple public datasets. The experimental results reveal that this method outperforms others in tracking performance under various challenging conditions.https://www.aimspress.com/article/doi/10.3934/era.2025068uav object trackingspatial saliency perceptionpositive expert groupspatio-temporal joint constraints |
| spellingShingle | Changhui Wu Jinrong Shen Kaiwei Chen Yingpin Chen Yuan Liao UAV object tracking algorithm based on spatial saliency-aware correlation filter Electronic Research Archive uav object tracking spatial saliency perception positive expert group spatio-temporal joint constraints |
| title | UAV object tracking algorithm based on spatial saliency-aware correlation filter |
| title_full | UAV object tracking algorithm based on spatial saliency-aware correlation filter |
| title_fullStr | UAV object tracking algorithm based on spatial saliency-aware correlation filter |
| title_full_unstemmed | UAV object tracking algorithm based on spatial saliency-aware correlation filter |
| title_short | UAV object tracking algorithm based on spatial saliency-aware correlation filter |
| title_sort | uav object tracking algorithm based on spatial saliency aware correlation filter |
| topic | uav object tracking spatial saliency perception positive expert group spatio-temporal joint constraints |
| url | https://www.aimspress.com/article/doi/10.3934/era.2025068 |
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