Cross-Level Attention Multi-Scale Context-Enhanced Crowd Counting Network for Transportation Cyber-Physical Systems

In transportation cyber-physical systems, real-time calculations of crowd density can provide traffic managers with important decision support to help optimise traffic flow and prevent congestion and safety accidents. However, crowd density estimation in complex traffic scenarios requires a comprehensive response to the challenges of background noise interference, dense occlusion, and scale variation. To address these issues, we propose a novel cross-level attention multi-scale context-enhanced crowd counting network (CAMCNet). First, we explore the feasibility of the ResNeXt network as a backbone for crowd counting. The residual structure of the ResNeXt model facilitates the generation of hierarchical scale features. Second, the high-level features extracted by this backbone lack detailed information, hindering the acquisition of accurate spatial location information of the crowd. Therefore, we present a high-level feature enhancement module to further refine the perception of multi-scale context and orientation features. Third, we design a cross-level attention feature fusion module to adaptively aggregate enhanced deep features and shallow features to better cope with the challenges of complex background interference and scale variations simultaneously. Finally, to enhance the robustness of network training, we introduce smooth L1 loss into the overall loss function. On four benchmark datasets, extensive experiments show that CAMCNet outperforms the vast majority of the most advanced methods. In particular, CAMCNet achieves state-of-the-art performance on two large-scale mainstream benchmarks (UCF-QNRF and NWPU) in terms of the MAE.