BTMTrack: Robust RGB-T tracking via dual-template bridging and temporal-modal candidate elimination

RGB-T tracking leverages the complementary strengths of RGB and thermal infrared (TIR) modalities to handle challenging scenarios, such as low illumination and adverse weather conditions. However, existing methods often struggle to effectively integrate temporal information and perform efficient cross-modal interactions, limiting their adaptability to dynamic targets. In this paper, we propose BTMTrack, a novel RGB-T tracking framework. At its core lies a dual-template backbone and a Temporal-Modal Candidate Elimination (TMCE) strategy. The dual-template backbone enables the effective integration of temporal information. At the same time, the TMCE strategy guides the model to focus on target-relevant tokens by evaluating temporal and modal correlations through attention correlation maps across different modalities. This not only reduces computational overhead but also mitigates the influence of irrelevant background noise. Building on this foundation, we introduce the Temporal Dual-Template Bridging (TDTB) module, which utilizes a cross-modal attention mechanism to process dynamically filtered tokens, thereby enhancing precise cross-modal fusion. This approach further strengthens the interaction between templates and the search region. Extensive experiments conducted on three benchmark datasets demonstrate the effectiveness of BTMTrack. Our method achieves state-of-the-art performance, with a 72.3% precision rate on the LasHeR test set and competitive results on the RGBT210 and RGBT234 datasets.