Xiaoyuan Yu, Yanyan Liang, Xuxin Lin, Jun Wan, Tian Wang, and Hong-Ning Dai
IEEE Transactions on Intelligent Transportation Systems, 2022, doi: 10.1109/TITS.2022.3178848
Publication year: 2022


Context prediction plays a crucial role in implementing autonomous driving applications. As one of important context-prediction tasks, crowd-and-vehicle counting is critical for achieving real-time traffic and crowd analysis, consequently facilitating decision-making processes for autonomous vehicles. However, the completion of crowd-and-vehicle counting also faces challenges, such as large-scale variations, imbalanced data distribution, and insufficient local patterns. To tackle these challenges, we put forth a novel frequency feature pyramid network (FFPNet) in this paper. Our proposed FFPNet extracts the multi-scale information by frequency feature pyramid module, which can tackle the issue of large-scale variations. Meanwhile, the frequency feature pyramid module uses different frequency branches to obtain different scale information. We also adopt the attention mechanism to strength the extraction of different scale information. Moreover, we devise a novel loss function, namely global-local consistency loss, to address the existing problems of imbalanced data distribution and insufficient local patterns. Furthermore, we conduct extensive experiments on six datasets to evaluate our proposed FFPNet. It is worth mentioning that we also construct a novel crowd-and-vehicle dataset (CROVEH), which is the only dataset that contains both crowd-and-vehicle annotations. The experimental results show that FFPNet achieves the best performance on different backbones, e.g., 52.69 mean absolute error (MAE) on P2PNet with FFP module. The codes are available at: https://github.com/MUST-AI-Lab/FFPNet.


  author={Yu, Xiaoyuan and Liang, Yanyan and Lin, Xuxin and Wan, Jun and Wang, Tian and Dai, Hong-Ning},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={Frequency Feature Pyramid Network With Global-Local Consistency Loss for Crowd-and-Vehicle Counting in Congested Scenes}, 

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