Three-Stream RGB-T Crowd-Counting Network With Feature Transfer and Enhanced Attention

Crowd counting using RGB images has made significant progress in practical applications, but it often struggles in low-light conditions. Recent advancements in infrared sensor technology have introduced thermal imaging as a powerful complement to RGB-based methods, allowing for more accurate identification of individuals in dark environments. However, effectively integrating RGB and thermal data remains challenging due to their inherent differences, and both image types are susceptible to background noise, which can blur distinctions between pedestrians and background elements. To address these challenges, we propose a novel three-stream RGB-T crowd counting network, termed the Feature Transfer and Enhanced Attention network (FENet). It incorporates two key modules: the Feature-Integrated Transfer (FIT) module and the Attention-Enhanced (AE) module. The FIT module is designed to fuse modality-specific features from both the RGB and thermal streams into a unified representation, facilitating progressively refined cross-modal complementary information through continuous feature propagation. To increase the sensitivity to crowd location, the AE module is introduced to explore the geometric dependencies between the head and surrounding regions (such as background noise) by perceiving the influence of different regions on the head, ensuring that the head receives a higher attention weight. Extensive experiments on the RGBT-CC dataset demonstrate that our method achieves superior performance in RGB-T crowd counting. Moreover, our approach also shows excellent performance on the ShanghaiTechRGBD dataset, confirming its generalizability across different modalities.