Multi-Column Atrous Convolutional Neural Network for Counting Metro Passengers

Abstract

We propose a symmetric method of accurately estimating the number of metro passengers from an individual image. To this end, we developed a network for metro-passenger counting called MPCNet, which provides a data-driven and deep learning method of understanding highly congested scenes and accurately estimating crowds, as well as presenting high-quality density maps. The proposed MPCNet is composed of two major components: A deep convolutional neural network (CNN) as the front end, for deep feature extraction; and a multi-column atrous CNN as the back-end, with atrous spatial pyramid pooling (ASPP) to deliver multi-scale reception fields. Existing crowd-counting datasets do not adequately cover all the challenging situations considered in our work. Therefore, we collected specific subway passenger video to compile and label a large new dataset that includes 346 images with 3475 annotated heads. We conducted extensive experiments with this and other datasets to verify the effectiveness of the proposed model. Our results demonstrate the excellent performance of the proposed MPCNet.