Lightweight Multiscale Perception and Global Context Guidance Network for Crowd Counting in Congested Scene

Accurate measurement of crowd density plays a critical role in public safety and intelligent transportation systems. In practical scenarios, cameras and embedded devices serve as essential instruments for real-time measurement. However, the discrepancy between the heavy network architecture frequently utilized for high-precision counting tasks and the constrained computational resource inherent in edge device has emerged as a significant challenge. In order to address this challenge, we propose an effective encoder-decoder based network, named lightweight multiscale perception and global context guidance network. Specifically, lightweight MobileOne network is applied as backbone for efficient feature extraction. Adaptive large kernel attention module is proposed for multiscale spatial hierarchy relationship extraction. Global context guidance module is designed for the fusion of multi-level features, allowing the extraction of effective global information from high-level feature to propagate to low-level feature. Experiments on four crowd counting datasets demonstrate that the proposed model is able to achieve comparable counting performance while maintaining small model size and low inference latency. The proposed model is deployed on Nvidia Jetson Nano and applied in edge density estimation of non-motorized transportation participants in Nanjing, providing important data foundation for traffic signal timing optimization.