Metro passengers counting and density estimation via dilated-transposed fully convolutional neural network

Abstract

Metro passenger counting and density estimation are crucial for traffic scheduling and risk prevention. Although deep learning has achieved great success in passenger counting, most existing methods ignore fundamental appearance information, leading to density maps of low quality. To address this problem, we propose a novel counting method called “dilated-transposed fully convolution neural network” (DT-CNN), which combines a feature extraction module (FEM) and a feature recovery module (FRM) to generate high-quality density maps and accurately estimate passenger counts in highly congested metro scenes. Specifically, the FEM is composed of a CNN, and a set of dilated convolutional layers extract 2D features relevant to scenes containing crowded human objects. Then, the resulting density map produced by the FEM is processed by the FRM to learn potential features, which is used to restore feature map pixels. The DT-CNN is end-to-end trainable and independent of the backbone fully convolutional network architecture. In addition, we introduce a new metro passenger counting dataset (Zhengzhou_MT++) that contains 396 images with 3,978 annotations. Extensive experiments conducted on self-built datasets and three representative crowd-counting datasets show the proposed method achieves superior performance relative to other state-of-the-art methods in terms of counting accuracy and density map quality. The Zhengzhou MT++ dataset is available at https://github.com/YellowChampagne/Zhengzhou_MT .