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结合语义和多层特征融合的行人检测

储珺 束雯 周子博 缪君 冷璐

储珺, 束雯, 周子博, 缪君, 冷璐. 结合语义和多层特征融合的行人检测. 自动化学报, 2022, 48(1): 282−291 doi: 10.16383/j.aas.c200032
引用本文: 储珺, 束雯, 周子博, 缪君, 冷璐. 结合语义和多层特征融合的行人检测. 自动化学报, 2022, 48(1): 282−291 doi: 10.16383/j.aas.c200032
Chu Jun, Shu Wen, Zhou Zi-Bo, Miao Jun, Leng Lu. Combining semantics with multi-level feature fusion for pedestrian detection. Acta Automatica Sinica, 2022, 48(1): 282−291 doi: 10.16383/j.aas.c200032
Citation: Chu Jun, Shu Wen, Zhou Zi-Bo, Miao Jun, Leng Lu. Combining semantics with multi-level feature fusion for pedestrian detection. Acta Automatica Sinica, 2022, 48(1): 282−291 doi: 10.16383/j.aas.c200032

结合语义和多层特征融合的行人检测

doi: 10.16383/j.aas.c200032
基金项目: 国家自然科学基金 (62162045, 61866028), 江西省重点研发计划项目 (20192BBE50073), 研究生创新基金(YC2018094) 资助
详细信息
    作者简介:

    储珺:江西省图像处理与模式识别重点实验室(南昌航空大学)教授. 主要研究方向为计算机视觉, 模式识别和深度学习. 本文通信作者. E-mail: chujun99602@163.com

    束雯:江西省图像处理与模式识别重点实验室(南昌航空大学)硕士研究生. 主要研究方向为图像处理, 计算机视觉. E-mail: shuwen0418@163.com

    周子博:江西省图像处理与模式识别重点实验室(南昌航空大学)硕士研究生. 主要研究方向为图像处理, 计算机视觉. E-mail: abaabc13@163.com

    缪君:江西省图像处理与模式识别重点实验室(南昌航空大学)副教授. 主要研究方向为计算机视觉, 3D重建和模式识别. E-mail: miaojun@nchu.edu.cn

    冷璐:江西省图像处理与模式识别重点实验室(南昌航空大学)副教授. 主要研究方向为计算机视觉, 模式识别和生物特征模板保护. E-mail: leng@nchu.edu.cn

Combining Semantics With Multi-level Feature Fusion for Pedestrian Detection

Funds: Supported by National Natural Science Foundation of China (62162045, 61866028), Jiangxi Key Research and Development Project (20192BBE50073), Innovation Foundation for Postgraduate (YC2018094)
More Information
    Author Bio:

    CHU Jun Professor at Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition (Nanchang Hangkong University). Her research interest covers computer vision, pattern recognition, and deep learning. Corresponding author of this paper

    SHU Wen Master student at Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition (Nanchang Hangkong University). Her research interest covers image processing and computer vision

    ZHOU Zi-Bo Master student at Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition (Nanchang Hangkong University). His research interest covers image processing and computer vision

    MIAO Jun Associate professor at Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition (Nanchang Hangkong University). His research interest covers computer vision, 3D reconstruction, and pattern recognition

    LENG Lu Associate professor at Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition (Nanchang Hangkong University). His research interest covers computer vision, pattern recognition, and biometric template protection

  • 摘要: 遮挡及背景中相似物干扰是行人检测准确率较低的主要原因. 针对该问题, 提出一种结合语义和多层特征融合(Combining semantics with multi-level feature fusion, CSMFF)的行人检测算法. 首先, 融合多个卷积层特征, 并在融合层上添加语义分割, 得到的语义特征与相应的卷积层连接作为行人位置的先验信息, 增强行人和背景的辨别性. 然后, 在初步回归的基础上构建行人二次检测模块(Pedestrian secondary detection module, PSDM), 进一步排除误检物体. 实验结果表明, 所提算法在数据集Caltech和CityPersons上漏检率(Miss rate, MR)为7.06 %和11.2 %. 该算法对被遮挡的行人具有强鲁棒性, 同时可方便地嵌入到其他检测框架.
  • 图  1  本文算法框架

    Fig.  1  Overview of our proposed framework

    图  2  基于目标框和物体轮廓为边界的逐像素分割结果

    Fig.  2  The pixel-by-pixel segmentation results based on object box boundary and object contour boundary

    图  3  添加语义分割前后Conv5_3层的特征可视化对比

    Fig.  3  Visual comparison of features of Conv5_3 layer before and after adding semantic segmentation

    图  4  CSMFF与各种对比算法在Caltech测试数据集上MR-FPPI变化

    Fig.  4  The variations of MR-FPPI of our proposed CSMFF with state-of-the-art approaches on the Caltech test dataset

    表  1  Caltech数据集中部分子集的划分标准

    Table  1  Evaluation settings for partial subsets of the Caltech dataset

    子集行人高度 (Height)行人被遮挡程度 (Occlusion)
    Reasonable$ > $50 PXsocc$ < $0.35
    Partial$ > $50 PXs0.10$ < $occ$ \le $0.35
    Heavy$ > $50 PXs0.35$ < $occ$ \le $0.80
    下载: 导出CSV

    表  2  CityPersons数据集中部分子集的划分标准

    Table  2  Evaluation settings for partial subsets of the CityPersons dataset

    子集行人高度 (Height)行人被遮挡程度 (Occlusion)
    Bare$ > $50 PXsocc$ \le $0.10
    Reasonable$ > $50 PXsocc$ < $0.35
    Partial$ > $50 PXs0.10$ < $occ$ \le $0.35
    Heavy$ > $50 PXs0.35$ < $occ$ \le $0.80
    下载: 导出CSV

    表  3  在Caltech测试数据集上对比算法性能以及运行速度比较

    Table  3  Performance and runtime comparisons of our proposed CSMFF with state-of-the-art approaches on the Caltech test dataset

    方法Reasonable MR (%)Partial MR (%)Heavy MR (%)速度 (s/帧)
    PL-CNN[16]12.4016.68
    Faster R-CNN$ + $ATT[32]10.3322.2945.18
    MS-CNN[10]9.9519.2459.940.40
    RPN$ + $BF[13]9.5824.2374.360.60
    AdaptFasterRCNN[14]9.1826.5557.58
    F-DNN[21]8.6515.4155.130.30
    PCN[20]8.4516.0955.81
    F-DNN$ + $SS[21]8.1815.1153.762.48
    CSMFF7.0614.3650.620.12
    下载: 导出CSV

    表  4  在CityPersons测试数据集上不同算法性能比较

    Table  4  Performance comparison of our proposed CSMFF with state-of-the-art approaches on the CityPersons test dataset

    方法骨干网络Bare MR (%)Reasonable MR (%)Partial MR (%)Heavy MR (%)
    TLL[33]ResNet-5010.015.517.253.6
    Repulsion Loss[34]ResNet-507.613.216.856.9
    LBST[35]ResNet-5012.853.7
    CC-CNN[36]VGG-168.211.814.1
    OR-CNN[37]VGG-166.712.815.355.7
    Faster R-CNN[14]VGG-1615.4
    CSMFFVGG-167.511.213.450.1
    下载: 导出CSV

    表  5  在Caltech测试数据集上融合不同卷积层的性能

    Table  5  Performance of fusing different convolutional layers on the Caltech test dataset

    卷积层 MR (%)
    Conv2_2Conv3_3Conv4_3Conv5_3PFEMCSMFF
    12.227.06
    32.4218.15
    18.7211.79
    下载: 导出CSV

    表  6  在Caltech数据集上测试每个组件的消融实验

    Table  6  Ablation experiments for testing each component on the Caltech dataset

    组件选择
    Faster R-CNN
    多层特征融合
    语义分割分支
    PSDM
    PFEM MR (%)14.9313.2712.5812.22
    CSMFF MR (%)12.119.538.687.06
    下载: 导出CSV
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  • 收稿日期:  2020-01-16
  • 录用日期:  2020-06-01
  • 刊出日期:  2022-01-25

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