Multi-scale dilated convolution of convolutional neural network for crowd counting

Abstract

Growing numbers of crowd density estimation methods have been developed in scene monitoring, crowd safety and on-site management scheduling. We proposed a method for density estimation of a single static image based on convolutional neural network naming Multi-scale Dilated Convolution of Convolutional Neural Network (Multi-scale-CNN). The proposed method employed the method of density maps regression to learn the mapping relationship between single-image and density maps through convolutional neural network. The adopted network structure is composed of two major components to adapt changes of characters scales in crowd images, a convolutional neural network for the general feature extraction and the other is multi-scale dilated convolution for disposing the scale change problem. It is insufficient for currently study that tackled the multi-column or multi-input convolutional neural networks to solve multi-scale problems. Our method utilizes a single-column network to extract features and combines multi-scale dilated convolution to aggregate multi-scale information to address the shortcomings of two networks. The multi-scale dilated convolution module aggregates multi-scale context information systematically by making use of dilated convolution without reducing the receiving domain, thereby integrate the underlying detail information into the high-level semantic features to promote the perception and counting ability of network for small targets. This paper demonstrates the proposed network structure in ShanghaiTech dataset, UCF_CC_50 dataset and worldexpo’10 dataset, and compares the results with numbers of current mainstream crowd counting algorithms, proves that our method surpasses current state-of-the-art methods and has excellent counting accuracy and robustness. The training and testing codes of our method models can be downloaded at https://github.com/doctorwgd/Multi-scale-CNN.