  Most state-of-the-art crowd counting methods use color (RGB) images to learn
the density map of the crowd. However, these methods often struggle to achieve
higher accuracy in densely crowded scenes with poor illumination. Recently,
some studies have reported improvement in the accuracy of crowd counting models
using a combination of RGB and thermal images. Although multimodal data can
lead to better predictions, multimodal data might not be always available
beforehand. In this paper, we propose the use of generative adversarial
networks (GANs) to automatically generate thermal infrared (TIR) images from
color (RGB) images and use both to train crowd counting models to achieve
higher accuracy. We use a Pix2Pix GAN network first to translate RGB images to
TIR images. Our experiments on several state-of-the-art crowd counting models
and benchmark crowd datasets report significant improvement in accuracy.
