Crowd-Counting of Images in Rainy Weather Based on GAN-U-Net Model and Multi-column CNN Counting Approaches

A count of crowds tries to determine the total number of persons in crowded places in order to avoid disruption of the safety system and maintain crowd safety. Accurate estimation of the size of crowds is a challenging process because of numerous challenges like occlusion, complicated backgrounds, varying sizes, uneven distribution, alteration of lighting, and adaptable weather. In this study, we propose a solution to two crowd counting challenges: poor weather such as rain and image scaling. We investigate crowd counting in the rainy Shanghai-Tec images. We adopt the GAN-U-Net de-raining model and the SAS-Net [1] and SGA-Net [2] crowd-counting models in our study.