Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion

Abstract: The main problem in RGB-T tracking is the correct and optimal merging of the cross-modal features of visible and thermal images. Some previous methods either do not fully exploit the potential of RGB and TIR information for channel and spatial feature fusion or lack a direct interaction between the template and the search area, which limits the model's ability to fully utilize the original semantic information of both modalities. To address these limitations, we investigate how to achieve a direct fusion of cross-modal channels and spatial features in RGB-T tracking and propose CSTNet. It uses the Vision Transformer (ViT) as the backbone and adds a Joint Spatial and Channel Fusion Module (JSCFM) and Spatial Fusion Module (SFM) integrated between the transformer blocks to facilitate cross-modal feature interaction. The JSCFM module achieves joint modeling of channel and multi-level spatial features. The SFM module includes a cross-attention-like architecture for cross modeling and joint learning of RGB and TIR features. Comprehensive experiments show that CSTNet achieves state-of-the-art performance. To enhance practicality, we retrain the model without JSCFM and SFM modules and use CSNet as the pretraining weight, and propose CSTNet-small, which achieves 50% speedup with an average decrease of 1-2% in SR and PR performance. CSTNet and CSTNet-small achieve real-time speeds of 21 fps and 33 fps on the Nvidia Jetson Xavier, meeting actual deployment requirements. Code is available at https://github.com/LiYunfengLYF/CSTNet.