BigCounter: A Bidirectional-Guided Network With Scene-Semantics-Driven Fusion for RGB–Thermal Crowd Counting

Accurate crowd counting has become increasingly essential for public safety management and Internet of Video Things (IoVT) applications, driven by rapid population growth and urbanization. However, RGB-Thermal (RGB-T) crowd counting remains challenging due to poor recognition of small targets and degraded performance under extreme conditions such as low-light environments. To address these issues, we propose a bidirectional-guided network with scene-semantics driven fusion for RGB-T crowd counting (BigCounter) that enhances robustness and generalization in complex scenes. BigCounter comprises of three parallel branches: a primary branch, a dynamic illumination auxiliary enhancement branch (DIAEB), and a high-resolution auxiliary enhancement branch (HAEB), which respectively improve robustness under illumination variations and accuracy for small target detection. Moreover, a cross-layer scene-driven fusion module (CLSFM) and a cross-modal semantic-driven fusion module (CMSFM) are designed to strengthen structural consistency and explore semantic complementarity between modalities. Through multi-branch collaboration and semantic-aware fusion, BigCounter significantly enhances feature representation. Extensive experiments on two benchmark RGB-T datasets demonstrate that BigCounter achieves superior accuracy and generalization compared with state-of-the-art methods.