RGB-T tracking of efficient feature maps via dual-stream Siamese network

Target tracking relying solely on visible images can be unreliable under adverse lighting conditions. In contrast, infrared images, capturing thermal radiation, remain unaffected by such factors. This complementarity has spurred the development of numerous RGB-T tracking methods. However, existing approaches often neglect effective feature extraction across various levels and overlook the computational demands of self-attention mechanisms. Addressing these challenges, we propose SiamEFM, a twin network-based RGB-T tracking algorithm. Our method leverages the Recurrent Cross-Circular Attention (RCCA) mechanism to enhance pixel-level representations, integrates modal information through a feature fusion network, and employs Layer Attention to consolidate features across different levels. Experimental validation on the GTOT dataset demonstrates the competitive performance of our tracker.