Hybrid Graph Neural Networks for Crowd Counting


  • Ao Luo University of Electronic Science and Technology of China
  • Fan Yang Inception Institute of Artificial Intelligence
  • Xin Li Inception Institute of Artificial Intelligence
  • Dong Nie University of North Carolina at Chapel Hill
  • Zhicheng Jiao University of Pennsylvania
  • Shangchen Zhou Nanyang Technological University
  • Hong Cheng University of Electronic Science and Technology of China




Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges: (i) multi-scale relations capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can capture and distill richer relations between nodes to obtain more powerful representations, providing robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art algorithms by a large margin.




How to Cite

Luo, A., Yang, F., Li, X., Nie, D., Jiao, Z., Zhou, S., & Cheng, H. (2020). Hybrid Graph Neural Networks for Crowd Counting. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11693-11700. https://doi.org/10.1609/aaai.v34i07.6839



AAAI Technical Track: Vision