Multi-scale Generative Adversarial Networks for Crowd Counting

We investigate generative adversarial networks as an effective solution to the crowd counting problem. These networks not only learn the mapping from crowd image to corresponding density map, but also learn a loss function to train this mapping. There are many challenges to the task of crowd counting, such as severe occlusions in extremely dense crowd scenes, perspective distortion, and high visual similarity between pedestrians and background elements. To address these problems, we proposed multi-scale generative adversarial network to generate high-quality crowd density maps of arbitrary crowd density scenes. We utilized the adversarial loss from discriminator to improve the quality of the estimated density map, which is critical to accurately predict crowd counts. The proposed multi-scale generator can extract multiple hierarchy features from the crowd image. The results showed that the proposed method provided better performance compared to current state-of-the-art methods.