Crowd Counting via Multi-view Scale Aggregation Networks

Crowd counting, aiming at estimating the total number of people in unconstrained crowded scenes, has increasingly received attention. But it is greatly challenged by the huge variation in people scale. In this paper, we propose a novel Multi-View Scale Aggregation Network (MVSAN), which handle the scale variation from feature, input and criterion view comprehensively. Firstly, we design a simple but effective Multi-Scale Feature Encoder, which exploits dilated convolution layers with various dilation rates to improve the representation ability and scale diversity of features. Secondly, we feed multiple scales of input images into networks to generate high-quality density maps in a coarse-to-fine manner. Finally, we propose a Multi-Scale Structural Similarity loss to force our networks to learn the local correlation of density maps. Extensive experiments on two standard benchmarks show that the proposed method can generate high-quality crowd density map and accurate count estimation, outperforming the state-of-the-art methods with a large margin.

1. Introduction

With the rapid growth of the urban population, public safety has become a great challenge in city management. Most safety control measures relied on crowd counting, which estimates the crowd number from images and surveillance videos. However, the large scale variance of people from massive street images from social networks and real-time surveillance, is still one of the main obstacles for accurate estimation.