RGB-T crowd counting method with multi-scale perception and infrared feature enhancement

In order to overcome the difficulty of crowd counting in low light, RGB-T crowd counting attempts to create maps of crowd density utilizing complimentary information from visual and thermal imagery. However, existing RGB-T crowd counting methods face issues such as scale variation and background interference during cross-modality information fusion. To tackle these challenges, we propose an RGB-T crowd counting method based on multi-scale perception and infrared feature enhancement (MSENet). Our approach presents an RGB-T feature fusion mechanism (RTFM) that creates an infrared enhancement structure to completely capture crowd information in thermal images and uses a multi-branch structure for multi-scale feature extraction. Additionally, we utilize dense connections and information divergence mechanisms to transfer complementary features to each modality, achieving a reusable expression of complementary features and enhanced modality features. We evaluate our proposed method on the RGBT-CC dataset and the ShanghaiTechRGBD dataset through comparative experiments. The results demonstrate that our method outperforms existing state-of-the-art approaches on the RGBT-CC dataset, exhibiting good accuracy, robustness and good generalization.