SMMT: Siamese Motion Mamba with Self-attention for Thermal Infrared Target Tracking
This work addresses thermal infrared target tracking, an incremental improvement for applications like surveillance and autonomous systems.
The paper tackled challenges in thermal infrared object tracking, such as occlusion and motion blur, by proposing SMMT, a Siamese Motion Mamba Tracker that integrates a bidirectional state-space model and self-attention, achieving superior performance on four TIR benchmarks.
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.