通过时间建模提升无人机跟踪的鲁棒性与效率
T-SiamTPN: Temporal Siamese Transformer Pyramid Networks for Robust and Efficient UAV Tracking
- 引入时序特征融合与注意力机制，增强目标追踪的时间一致性
- 在基准上成功率提升13.7%，精确度提高14.7%，实时运行达7.1帧/秒
- 适合资源受限设备部署，兼具高性能与低计算开销
由于尺度变化、动态背景、杂乱环境和频繁遮挡，空中目标跟踪仍具挑战。现有追踪器多关注空间线索，常忽视时序依赖，导致长期追踪与遮挡下性能受限。此外，基于相关性的孪生追踪器受线性相关操作限制，难以应对复杂非线性外观变化。为此，我们提出T-SiamTPN，一种在SiamTPN基础上引入显式时序建模的孪生追踪框架。通过时序特征融合与基于注意力的交互，增强时序一致性并生成更丰富的特征表示。实验表明，该方法在保持高效性的同时显著优于基线模型，在多个数据集上成功率达13.7%提升，精度提升14.7%。即使在资源受限的Jetson Nano上，仍可实现7.1帧/秒的实时运行，验证其在嵌入式场景中的适用性。结果表明，时序建模对孪生追踪至关重要，T-SiamTPN为航空目标跟踪提供了一种高效且强大的解决方案。
原文摘要 · Abstract (English)
Aerial object tracking remains a challenging task due to scale variations, dynamic backgrounds, clutter, and frequent occlusions. While most existing trackers emphasize spatial cues, they often overlook temporal dependencies, resulting in limited robustness in long-term tracking and under occlusion. Furthermore, correlation-based Siamese trackers are inherently constrained by the linear nature of correlation operations, making them ineffective against complex, non-linear appearance changes. To address these limitations, we introduce T-SiamTPN, a temporal-aware Siamese tracking framework that extends the SiamTPN architecture with explicit temporal modeling. Our approach incorporates temporal feature fusion and attention-based interactions, strengthening temporal consistency and enabling richer feature representations. These enhancements yield significant improvements over the baseline and achieve performance competitive with state-of-the-art trackers. Crucially, despite the added temporal modules, T-SiamTPN preserves computational efficiency. Deployed on the resource-constrained Jetson Nano, the tracker runs in real time at 7.1 FPS, demonstrating its suitability for real-world embedded applications without notable runtime overhead. Experimental results highlight substantial gains: compared to the baseline, T-SiamTPN improves success rate by 13.7% and precision by 14.7%. These findings underscore the importance of temporal modeling in Siamese tracking frameworks and establish T-SiamTPN as a strong and efficient solution for aerial object tracking. Code is available at: https://github.com/to/be/released
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