Volume 41 Issue 6
Dec.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(6): 107-113
WANG Liyuan, YAO Yuntao, JIA Yang, XIAO Jinsheng, LI Bijun. Crowd Count Neural Network Based on Attention Mechanism in Traffic Scenes[J]. Journal of Transport Information and Safety, 2023, 41(6): 107-113. doi: 10.3963/j.jssn.1674-4861.2023.06.012
Citation: WANG Liyuan, YAO Yuntao, JIA Yang, XIAO Jinsheng, LI Bijun. Crowd Count Neural Network Based on Attention Mechanism in Traffic Scenes[J]. Journal of Transport Information and Safety, 2023, 41(6): 107-113. doi: 10.3963/j.jssn.1674-4861.2023.06.012
shu

Crowd Count Neural Network Based on Attention Mechanism in Traffic Scenes

doi: 10.3963/j.jssn.1674-4861.2023.06.012
  • 1.

    CCCC Second Highway Consultants Co., LTD, Wuhan 430056, China

  • 2.

    School of Electronic Information, Wuhan University, Wuhan 430072, China

  • 3.

    Sichuan Highway Planning, Survey, Design and Research Institute Co., LTD., Chengdu 610041, China

  • 4.

    State Key Laboratory of Information Engineering in Surveying, mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • Received Date: 2023-08-18
    Available Online: 2024-04-03
    Abstract
  • Crowd count is an important task in computer vision. Crowd count task in traffic scenes plays a significant role in maintaining public traffic safety and achieving traffic intelligence. However, crowd count in public traffic scenes faces difficulties due to pedestrian occlusion and complex background. In order to achieve high accuracy crowd count, an attention-based crowd density estimation network is proposed. The network consists of three parts: a feature extraction module is designed to generate multi-scale feature maps, which can enhance the feature representation capability and improve the robustness to pedestrian scale variation of the network; an attention module is designed to suppress the background noise response and enhance the crowd feature response, generate the probability distribution of the crowd region in the feature map, which can enhance the ability of the network to distinguish the crowd region from the background region; a density estimation module is designed that guides the network to regress a high-resolution crowd density map under the constraint of attention mechanism, which can improve the sensitivity of the network to crowd regions. In addition, a background-aware structure loss function is designed to reduce the model false recognition rate and improve the model counting accuracy; meanwhile, a multi-level super-vision mechanism is adopted to guide the network for learning, which can help gradient back-propagation and reduce over-fitting, further improving the network's crowd count accuracy. Experiments are carried out on public dataset ShanghaiTech. Compared with the state-of-the-art algorithms, on ShanghaiTechA and ShanghaiTechB datasets, the mean absolute error (MAE) improves by 2.4% and 1.5%, and the mean square error (MSE) improves by 3.3% and 0.9%, respectively, which demonstrates the superior accuracy and robustness of the proposed algorithm in both crowded and sparse scenes. Experiments are also conducted on real scene dataset with MAE=7.7 and MSE=12.6, which proves the good applicability of the proposed algorithm.

     

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