Conditional RGB-T Fusion for Effective Crowd Counting

Abstract

A robust and efficient crowd counting framework forms a vital step towards the analysis of a crowded scene and finds its applications in social distance monitoring, traffic management, and video surveillance. We argue that visible RGB images fail to yield high-quality density maps owing to poor lighting conditions during the dark when an anomaly is more likely to occur. To tackle this scenario, we introduce a novel architecture Toggle-Fusion Network (TFNet) that effectively utilises a multimodal dataset, RGBT-CC, containing pairs of thermal and RGB images. Our approach eliminates the need for two branches for each of these modalities as proposed in the baseline by conditionally fusing the thermal and RGB images with the help of a toggle, saliency maps, and a rolling guidance filter. We conduct extensive experiments and delineate the importance of individual components of our method in the ablation study. TFNet, after evaluation on RGBT-CC, receives an RMSE value of 6.11 thereby establishing new state-of-the-art for the dataset. The code is made publicly available https://github.com/ShreyasSR/TFNet