Crowd Counting with Spatial Normalization Network

Abstract

Crowd counting, which requires to estimate crowd density from an image, is still a challenging task in computer vision. Most of the current methods are focused on large scale variation of people and ignore the huge distribution difference of crowd. To tackle these two problems together, we propose a novel framework named Spatial Normalization Network (SNNet). We normalize multi-scale features from parallel subnetworks to a particular scale and then fuse them to acquire rich spatial information for final accurate density map predictions. Furthermore, we propose a novel normalization layer called Spatial Group Normalization (SGN), which firstly split feature maps along the spatial dimension and then perform group-wise normalization. It’s useful to solve statistic shift problems caused by the great difference of distribution in crowd counting. Moreover, SGN can be naturally plugged into existing solutions and brings significant improvement in crowd counting. Our proposed SNNet achieves state-of-the-art performance on four challenging crowd counting datasets (ShanghaiTech, UCFQNRF, GCC and TRANCOS datasets), which demonstrates the effectiveness and robust feature learning capability of our methods.