Visual Prompt Multibranch Fusion Network for RGB-Thermal Crowd Counting

As population growth and urbanization continue, accurate crowd counting is increasingly important for public safety management and the Internet of Video Things (IOVT). However, RGB and thermal infrared (RGB-T) crowd counting still faces challenges in improving feature extraction capability for RGB streams and reducing multimodality differences. For this, we propose a visual prompt multibranch fusion network (VPMFNet) to tackle the above challenges. Specifically, to improve the ability of crowd analysis of the RGB stream in RGB-T crowd counting, through designing the prompt enhancement module, we take the prior features of head perception in the crowd as visual prompt cues to embed into the RGB stream. In terms of RGB and thermal image feature fusion, we fully reduce the modality differences from the perspectives of local fusion, global fusion, and multireceptive field fusion to accurately estimate the pedestrian number. Various experiments on two RGB-T crowd counting data sets demonstrate that our VPMFNet achieves a smaller estimation error in the number of pedestrians. Besides, our VPMFNet outperforms existing methods (i.e., multicolumn convolutional neural network, BL, SANet, UCNet, HDFNet, BBSNet, BL+IDAM, BL+CSCA, dual-branch enhanced feature fusion network, and GETANet) on the RGB-D data set. Our code will be released at
https://github.com/QSBAOYANGMU/VPMFNet
.