Multi Scale Attention Network for Crowd Counting

Abstract

Reasonable management and control of extra crowded scenes have become a hot topic in recent years. Counting people from density map generated from the object location annotations is an effective way to analyze crowd information and control crowds in severely congested scenes. In this paper, we propose a novel end-to-end crowd counting method called MSANet for crowd counting. MSANet consists of the VGG16 backbone as the fronted part, two branches as the back-end part, including the attention map extractor to predict crowd states (means with people or not), and density map branch to regress the density map. What is more, to obtain high-resolution density map, we combine different scale maps from the front part to the back-end part. On the design of the loss function, to enhance the resolution of the predicted map and its structural similarity to ground truth, we proposed a new loss function for crowd counting. The test result based on the public dataset ShanghaiTech and Subway Crowd Counting Dataset supported by the Nanjing Metro demonstrates the effectiveness of our method.