MC3Net: Multimodality Cross-Guided Compensation Coordination Network for RGB-T Crowd Counting

Owing to the expansion in processing of industrial information through advances in machine learning, the demand for accurate crowd counting in various applications is increasing. We propose a multimodality cross-guided compensation coordination network (MC $^{3}$ Net) for accurate red–green–blue and thermal (RGB-T) crowd counting. The network includes modules of intricate interactive fusion, feature difference compensation, and complementary attention enhancement. We use ConvNext as the backbone and process the three streams from RGB, thermal, and spliced RGB-T inputs. The multimodality data are sequentially guided and fused hierarchically, fully combining features extracted from the RGB and thermal images. Thereafter, difference compensation is applied to compress fusion and splicing features. Redundant information is removed. Then, feature mismatch is mitigated to enhance complementary information, reduce the loss of details, and finally obtain crowd statistics. Results from extensive experiments on the RGBT-CC dataset indicate the robustness and effectiveness of MC $^{3}$ Net, which also achieves high performance on the DroneRGBT dataset and ShanghaiTechRGBD dataset, outperforming existing crowd counting methods. The code and models are available at: https://github.com/WBangG/MC3Net.