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面向鱼眼图像的人群密度估计

杨家林 林春雨 聂浪 刘美琴 赵耀

杨家林, 林春雨, 聂浪, 等 . 面向鱼眼图像的人群密度估计[J]. 北京航空航天大学学报, 2022, 48(8): 1455-1463. doi: 10.13700/j.bh.1001-5965.2021.0520
引用本文: 杨家林, 林春雨, 聂浪, 等 . 面向鱼眼图像的人群密度估计[J]. 北京航空航天大学学报, 2022, 48(8): 1455-1463. doi: 10.13700/j.bh.1001-5965.2021.0520
YANG Jialin, LIN Chunyu, NIE Lang, et al. Crowd density estimation for fisheye images[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1455-1463. doi: 10.13700/j.bh.1001-5965.2021.0520(in Chinese)
Citation: YANG Jialin, LIN Chunyu, NIE Lang, et al. Crowd density estimation for fisheye images[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1455-1463. doi: 10.13700/j.bh.1001-5965.2021.0520(in Chinese)

面向鱼眼图像的人群密度估计

doi: 10.13700/j.bh.1001-5965.2021.0520
基金项目: 

国家自然科学基金 62172032

国家自然科学基金 61972028

详细信息
    通讯作者:

    林春雨, E-mail: cylin@bjtu.edu.cn

  • 中图分类号: TP391

Crowd density estimation for fisheye images

Funds: 

National Natural Science Foundation of China 62172032

National Natural Science Foundation of China 61972028

More Information
  • 摘要:

    针对传统人群密度估计方法在鱼眼图像畸变下不适用的问题,提出了一个面向鱼眼图像的人群密度估计方法,实现了在鱼眼镜头场景下对人流量的监控。在模型结构方面,引入了可变形卷积,提高了模型对鱼眼畸变的适应能力。在生成目标数据方面,利用鱼眼图像的畸变特点,基于高斯变换,对人群标注转换的密度图进行符合鱼眼畸变的分布匹配。在训练方面,对损失函数的计算进行了优化,避免了模型在训练中陷入局部最优解的问题。由于鱼眼人群计数的数据集比较匮乏,采集并标注了相应的数据集。通过主客观实验与经典方法进行了对比,所提方法在测试集中的平均绝对误差达3.78,低于对比方法,证明了面向鱼眼图像的人群密度估计方法的优越性。

     

  • 图 1  σ值对高斯分布的影响

    Figure 1.  Influence of σ on Gaussian distribution

    图 2  预测模型结构

    Figure 2.  Structure of prediction model

    图 3  方差值的变化对高斯分布的影响

    Figure 3.  Influence of covariance matrix on Gaussian distribution

    图 4  旋转效果

    Figure 4.  Effects of rotation

    图 5  变换效果

    Figure 5.  Transformation effect

    图 6  标注点与环境区域

    Figure 6.  Labeled points and environment areas

    图 7  MAE对比

    Figure 7.  Comparison of MAE

    图 8  人群密度图生成效果

    Figure 8.  Display of crowd density maps

    表  1  不同方法的最优结果

    Table  1.   The best result of different methods

    方法 MAE Bias
    D2CNet 6.18 0.44
    MCNN 12.21 0.88
    CSRNet 5.66 0.41
    Bayesian Loss 3.96 0.29
    本文方法 3.78 0.27
    下载: 导出CSV

    表  2  消融实验结果

    Table  2.   Ablation results

    对比方法 MAE Bias
    方案1 5.59 0.40
    方案2 4.22 0.30
    方案3 3.89 0.28
    对照组 3.78 0.27
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-06
  • 录用日期:  2021-10-01
  • 刊出日期:  2021-11-17

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