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.

翻译：大多数先进的人群计数方法使用彩色（RGB）图像来学习人群密度图。然而，这些方法在光照条件差的密集场景中往往难以获得更高精度。近期有研究报道，结合RGB图像与热红外图像可提升人群计数模型的准确性。虽然多模态数据能带来更优预测，但多模态数据可能无法预先获取。本文提出利用生成对抗网络（GAN）自动从彩色（RGB）图像生成热红外（TIR）图像，并同时使用两种图像训练人群计数模型以实现更高精度。我们首先采用Pix2Pix GAN网络将RGB图像转换为TIR图像。在多个先进人群计数模型和基准数据集上的实验表明，该方法显著提升了计数精度。