Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs
This addresses the problem of degraded crowd counting accuracy in fog, dust, and low-light conditions for applications requiring high reliability, though it is incremental as it adapts an existing denoising method to this domain.
The paper tackles crowd counting in harsh weather by using a Pix2Pix GAN to denoise images before counting, improving performance on noisy data as validated on the JHU-Crowd dataset.
Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs). The performance of the model heavily relies on the quality of the training data that constitutes crowd images. In harsh weather such as fog, dust, and low light conditions, the inference performance may severely degrade on the noisy and blur images. In this paper, we propose the use of Pix2Pix generative adversarial network (GAN) to first denoise the crowd images prior to passing them to the counting model. A Pix2Pix network is trained using synthetic noisy images generated from original crowd images and then the pretrained generator is then used in the inference engine to estimate the crowd density in unseen, noisy crowd images. The performance is tested on JHU-Crowd dataset to validate the significance of the proposed method particularly when high reliability and accuracy are required.