A crowd counting method via density map and counting residual estimation

Recently, state-of-the-art crowd counting methods have focused more on predicting a density map and then obtaining the final aggregated count. In 2018, a typical density map-based network for congested scene recognition called CSRNet was proposed, and it achieved better crowd counting performance than previous methods with a simple architecture. It utilizes the first 10 layers from VGG-16 as the front end and deploys dilated convolutional layers as the back-end to generate high-quality density maps. CSRNet has been demonstrated on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the World Expo'10 dataset, and the UCSD dataset) and delivered great performance. To obtain better performance, in this paper, we propose a small network as a new component that generates a counting residual estimation, and we combine our component with CSRNet. We demonstrate this combined network on three datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, and the World Expo'10 dataset) and compare the results with those of CSRNet. The results show that our method has significantly improved the results of CSRNet. Through a series of experiments, such as ablation experiments and control experiments, we demonstrate the effectiveness of our method. In the future, we will apply our method to other networks to achieve better results.