Multi-level Attention Network for Accurate Crowd Counting in Challenging Rail Transit Environment

Accurate crowd counting is a critical challenge for intelligent transportation management, especially in high-density rail transit stations. The existing methods often struggle with multi-scale feature adaptability and dense occlusion robustness in real-world complex settings, leading to reduced accuracy. To address these issues, we propose the multi-level attention network (MLANet), which enhances passenger flow counting and provides essential data to support passenger safety by integrating a multi-scale attention module (MSAM) and a dynamic Gaussian attention module (DGAM). MSAM leverages parallel convolution and spatial attention mechanisms to capture multi-scale crowd features effectively, whereas DGAM dynamically refines attention regions by adapting Gaussian distribution parameters to varying crowd densities. Additionally, by integrating transformer-based global attention, MLANet significantly enhances feature representation in complex crowd environments. To further enhance robustness, we design a hybrid loss function that combines Euclidean loss and dynamic Gaussian attention loss to optimize feature distribution learning. The experimental results demonstrate that MLANet reaches an advanced level on the ShanghaiTech A/B, University of Central Florida (UCF)-QNRF, Johns Hopkins University (JHU)-Crowd++ benchmark datasets and the self-built Railway-Station dataset. The proposed approach offers a novel solution for accurate crowd counting in high-density passenger flow environments and holds significant practical value for ensuring operational safety.