FASFF: Frequency domain information-aided spatial domain feature fusion for RGBT tracking

RGBT object tracking takes advantage of the complementary properties of RGB and thermal infrared (TIR) modalities. However, many existing methods focus on fusion within a single domain, either spatial or frequency, without fully exploiting the complementarity of multiple domains. This limits the interaction between domains and makes trackers less robust under severe conditions. To improve feature representation by leveraging both spatial and frequency information, we propose a Frequency domain information-Aided Spatial domain Feature Fusion framework for RGBT tracking (FASFF), which mainly consists of the Frequency domain Attention Enhancement Module (FAEM) and the Spatial-Frequency feature interaction Fusion Module (SFFM). The FAEM employs the Discrete Cosine Transform Attention block (DCTAttn), which highlights informative frequency components while integrating spatial and frequency domains information, and the Cross-modality Collaborative Attention block (CCAttn), which suppresses noise via cross-modal feature refinement to enhance intra-modality feature representation. The SFFM further harnesses the interaction between the spatial and frequency domains to collaboratively refine feature representation, leveraging complementary information from the frequency domain to enhance spatial domain representations. We evaluate the FASFF on three benchmark datasets: RGBT210, RGBT234, and LasHeR. The experimental results show that the FASFF maintains stable and reliable performance across different tracking scenarios, verifying its effectiveness for RGBT object tracking.