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Most state-of-the-art crowd counting methods use color (RGB) images to learnthe density map of the crowd. However, these methods often struggle to achievehigher accuracy in densely crowded scenes with poor illumination. Recently,some studies have reported improvement in the accuracy of crowd counting modelsusing a combination of RGB and thermal images. Although multimodal data canlead to better predictions, multimodal data might not be always availablebeforehand. In this paper, we propose the use of generative adversarialnetworks (GANs) to automatically generate thermal infrared (TIR) images fromcolor (RGB) images and use both to train crowd counting models to achievehigher accuracy. We use a Pix2Pix GAN network first to translate RGB images toTIR images. Our experiments on several state-of-the-art crowd counting modelsand benchmark crowd datasets report significant improvement in accuracy.

Multimodal Crowd Counting with Pix2Pix GANs