High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual Experts
Visual tracking from the unmanned aerial vehicle (UAV) perspective has been at the core of many low-altitude remote sensing applications. Most of the aerial trackers follow “tracking-by-detection” paradigms or their temporal-context-embedded variants, where the only visual appearance cue is encompas...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2237 |
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| author | Shichao Zhou Xiangpan Fan Zhuowei Wang Wenzheng Wang Yunpu Zhang |
| author_facet | Shichao Zhou Xiangpan Fan Zhuowei Wang Wenzheng Wang Yunpu Zhang |
| author_sort | Shichao Zhou |
| collection | DOAJ |
| description | Visual tracking from the unmanned aerial vehicle (UAV) perspective has been at the core of many low-altitude remote sensing applications. Most of the aerial trackers follow “tracking-by-detection” paradigms or their temporal-context-embedded variants, where the only visual appearance cue is encompassed for representation learning and estimating the spatial likelihood of the target. However, the variation of the target appearance among consecutive frames is inherently unpredictable, which degrades the robustness of the temporal context-aware representation. To address this concern, we advocate extra visual motion exhibiting predictable temporal continuity for complete temporal context-aware representation and introduce a dual-stream tracker involving explicit heterogeneous visual tracking experts. Our technical contributions involve three-folds: (1) high-order temporal context-aware representation integrates motion and appearance cues over a temporal context queue, (2) bidirectional cross-domain refinement enhances feature representation through cross-attention based mutual guidance, and (3) consistent decision-making allows for anti-drifting localization via dynamic gating and failure-aware recovery. Extensive experiments on four UAV benchmarks (UAV123, UAV123@10fps, UAV20L, and DTB70) illustrate that our method outperforms existing aerial trackers in terms of success rate and precision, particularly in occlusion and fast motion scenarios. Such superior tracking stability highlights its potential for real-world UAV applications. |
| format | Article |
| id | doaj-art-e838aaefca6d455a8844dce602792257 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e838aaefca6d455a8844dce6027922572025-08-20T02:36:33ZengMDPI AGRemote Sensing2072-42922025-06-011713223710.3390/rs17132237High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual ExpertsShichao Zhou0Xiangpan Fan1Zhuowei Wang2Wenzheng Wang3Yunpu Zhang4School of Information Communication Engineering, Beijing Information Science and Technology University, Beijing 100192, ChinaSchool of Information Communication Engineering, Beijing Information Science and Technology University, Beijing 100192, ChinaSchool of Information Communication Engineering, Beijing Information Science and Technology University, Beijing 100192, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Information Communication Engineering, Beijing Information Science and Technology University, Beijing 100192, ChinaVisual tracking from the unmanned aerial vehicle (UAV) perspective has been at the core of many low-altitude remote sensing applications. Most of the aerial trackers follow “tracking-by-detection” paradigms or their temporal-context-embedded variants, where the only visual appearance cue is encompassed for representation learning and estimating the spatial likelihood of the target. However, the variation of the target appearance among consecutive frames is inherently unpredictable, which degrades the robustness of the temporal context-aware representation. To address this concern, we advocate extra visual motion exhibiting predictable temporal continuity for complete temporal context-aware representation and introduce a dual-stream tracker involving explicit heterogeneous visual tracking experts. Our technical contributions involve three-folds: (1) high-order temporal context-aware representation integrates motion and appearance cues over a temporal context queue, (2) bidirectional cross-domain refinement enhances feature representation through cross-attention based mutual guidance, and (3) consistent decision-making allows for anti-drifting localization via dynamic gating and failure-aware recovery. Extensive experiments on four UAV benchmarks (UAV123, UAV123@10fps, UAV20L, and DTB70) illustrate that our method outperforms existing aerial trackers in terms of success rate and precision, particularly in occlusion and fast motion scenarios. Such superior tracking stability highlights its potential for real-world UAV applications.https://www.mdpi.com/2072-4292/17/13/2237unmanned aerial vehiclelow-altitude remote sensingoptical trackingtemporal reasoningmotion analysisdecision-making |
| spellingShingle | Shichao Zhou Xiangpan Fan Zhuowei Wang Wenzheng Wang Yunpu Zhang High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual Experts Remote Sensing unmanned aerial vehicle low-altitude remote sensing optical tracking temporal reasoning motion analysis decision-making |
| title | High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual Experts |
| title_full | High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual Experts |
| title_fullStr | High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual Experts |
| title_full_unstemmed | High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual Experts |
| title_short | High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual Experts |
| title_sort | high order temporal context aware aerial tracking with heterogeneous visual experts |
| topic | unmanned aerial vehicle low-altitude remote sensing optical tracking temporal reasoning motion analysis decision-making |
| url | https://www.mdpi.com/2072-4292/17/13/2237 |
| work_keys_str_mv | AT shichaozhou highordertemporalcontextawareaerialtrackingwithheterogeneousvisualexperts AT xiangpanfan highordertemporalcontextawareaerialtrackingwithheterogeneousvisualexperts AT zhuoweiwang highordertemporalcontextawareaerialtrackingwithheterogeneousvisualexperts AT wenzhengwang highordertemporalcontextawareaerialtrackingwithheterogeneousvisualexperts AT yunpuzhang highordertemporalcontextawareaerialtrackingwithheterogeneousvisualexperts |