Thermal infrared (TIR) object tracking often suffers from challenges such as target occlusion, motion blur, and background clutter, which significantly degrade the performance of trackers. To address these issues, this paper pro-poses a novel Siamese Motion Mamba Tracker (SMMT), which integrates a bidirectional state-space model and a self-attention mechanism. Specifically, we introduce the Motion Mamba module into the Siamese architecture to ex-tract motion features and recover overlooked edge details using bidirectional modeling and self-attention. We propose a Siamese parameter-sharing strate-gy that allows certain convolutional layers to share weights. This approach reduces computational redundancy while preserving strong feature represen-tation. In addition, we design a motion edge-aware regression loss to improve tracking accuracy, especially for motion-blurred targets. Extensive experi-ments are conducted on four TIR tracking benchmarks, including LSOTB-TIR, PTB-TIR, VOT-TIR2015, and VOT-TIR 2017. The results show that SMMT achieves superior performance in TIR target tracking.

翻译：热红外目标跟踪常面临目标遮挡、运动模糊和背景干扰等挑战，这些因素显著降低了跟踪器的性能。为应对这些问题，本文提出了一种新颖的孪生运动Mamba跟踪器，该模型融合了双向状态空间模型与自注意力机制。具体而言，我们在孪生架构中引入运动Mamba模块，通过双向建模和自注意力机制提取运动特征并恢复被忽略的边缘细节。我们提出了一种孪生参数共享策略，使特定卷积层能够共享权重。该方法在保持强大特征表征能力的同时减少了计算冗余。此外，我们设计了一种运动边缘感知回归损失函数，以提升跟踪精度，特别是在处理运动模糊目标时。我们在LSOTB-TIR、PTB-TIR、VOT-TIR2015和VOT-TIR2017四个热红外跟踪基准数据集上进行了大量实验。结果表明，SMMT在热红外目标跟踪任务中取得了优越的性能。