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基于特征通道和空间联合注意机制的遮挡行人检测方法

陈勇 刘曦 刘焕淋

引用本文: 陈勇, 刘曦, 刘焕淋. 基于特征通道和空间联合注意机制的遮挡行人检测方法[J]. 电子与信息学报, 2020, 42(6): 1486-1493. doi: 10.11999/JEIT190606 shu
Citation:  Yong CHEN, Xi LIU, Huanlin LIU. Occluded Pedestrian Detection Based on Joint Attention Mechanism of Channel-wise and Spatial Information[J]. Journal of Electronics and Information Technology, 2020, 42(6): 1486-1493. doi: 10.11999/JEIT190606 shu

基于特征通道和空间联合注意机制的遮挡行人检测方法

    作者简介: 陈勇: 男,1963年生,博士,教授,研究方向为图像处理;
    刘曦: 男,1993年生,硕士生,研究方向为行人目标检测;
    刘焕淋: 女,1970年生,博士,教授,研究方向为信号处理等方面的研究
    通讯作者: 陈勇,chenyong@cqupt.edu.cn
  • 基金项目: 国家自然科学基金(51977021)

摘要: 遮挡是行人检测任务中导致漏检发生的主要原因之一,对检测器性能造成了不利影响。为了增强检测器对于遮挡行人目标的检测能力,该文提出一种基于特征引导注意机制的单级行人检测方法。首先,设计一种特征引导注意模块,在保持特征通道间的关联性的同时保留了特征图的空间信息,引导模型关注遮挡目标可视区域;然后,通过注意模块融合浅层和深层特征,从而提取到行人的高层语义特征;最后,将行人检测作为一种高层语义特征检测问题,通过激活图的形式预测得到行人位置和尺度,并生成最终的预测边界框,避免了基于先验框的预测方式所带来的额外参数设置。所提方法在CityPersons数据集上进行了测试,并在Caltech数据集上进行了跨数据集实验。结果表明该方法对于遮挡目标检测准确度优于其他对比算法。同时该方法实现了较快的检测速度,取得了检测准确度和速度的平衡。

English

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  • 图 1  模型总体结构

    图 2  注意模块总体结构

    图 3  特征通道注意模块结构

    图 4  空间关注模块

    图 5  行人解析网络

    图 6  可视化位置预测热图

    图 7  Caltech跨数据库实验

    表 1  验证实验条件设置

    R (Reasonable)HO (Heavy Occlusion)R+HO (Reasonable+Heavy Occlusion)
    $v \in [0.65,\infty )$$v \in [0.20,0.65]$$v \in [0.20,\infty )$
    下载: 导出CSV

    表 2  注意网络验证结果MR–2(%)

    方法RHOR+HO
    文献[16]16.056.738.2
    Baseline12.141.138.1
    Baseline+CA11.839.237.8
    Baseline+CA+SA11.638.537.3
    下载: 导出CSV

    表 3  CityPersons数据集测试结果MR-2(%)

    方法主干网络ReasonableHeavyPartialBare测试时间(s)
    OR-CNN[11]VGG-1612.855.715.36.7
    FasterRCNN[21]VGG-1615.4
    ALFNet[8]ResNet-5012.051.911.48.40.27
    CSP[9]ResNet-5011.049.310.47.30.33
    CAFL[13]ResNet-5011.450.412.17.6
    PedJointNet[14]ResNet-5013.552.1
    TLL[20]ResNet-5015.553.617.210.0
    RepLoss[23]ResNet-5013.256.916.87.6
    本文方法ResNet-5011.647.69.87.50.22
    下载: 导出CSV
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文章相关
  • 通讯作者:  陈勇, chenyong@cqupt.edu.cn
  • 收稿日期:  2019-08-09
  • 录用日期:  2020-02-18
  • 网络出版日期:  2020-03-13
  • 刊出日期:  2020-06-01
通讯作者: 陈斌, bchen63@163.com
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