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实时行人检测预警系统

程如中 赵勇 王执中 许家尧 王新安

程如中, 赵勇, 王执中, 许家尧, 王新安. 实时行人检测预警系统[J]. 交通运输工程学报, 2012, 12(5): 110-118. doi: 10.19818/j.cnki.1671-1637.2012.05.015
引用本文: 程如中, 赵勇, 王执中, 许家尧, 王新安. 实时行人检测预警系统[J]. 交通运输工程学报, 2012, 12(5): 110-118. doi: 10.19818/j.cnki.1671-1637.2012.05.015
CHENG Ru-zhong, ZHAO Yong, WANG Zhi-zhong, XU Jia-yao, WANG Xin-an. Real-time pedestrian detecting and warning system[J]. Journal of Traffic and Transportation Engineering, 2012, 12(5): 110-118. doi: 10.19818/j.cnki.1671-1637.2012.05.015
Citation: CHENG Ru-zhong, ZHAO Yong, WANG Zhi-zhong, XU Jia-yao, WANG Xin-an. Real-time pedestrian detecting and warning system[J]. Journal of Traffic and Transportation Engineering, 2012, 12(5): 110-118. doi: 10.19818/j.cnki.1671-1637.2012.05.015

实时行人检测预警系统

doi: 10.19818/j.cnki.1671-1637.2012.05.015
基金项目: 

香港特别行政区创新科技署创新及科技支援计划粤港创新圈项目 GHP/057/08AP

详细信息
    作者简介:

    程如中(1974-), 男, 黑龙江哈尔滨人, 北京大学理学博士研究生, 从事交通控制系统研究

    王新安(1963-), 男, 河南驻马店人, 北京大学教授, 工学博士

  • 中图分类号: U491.6

Real-time pedestrian detecting and warning system

More Information
  • 摘要: 针对重特大交通事故中的行人保护问题, 提出了基于侧面行人特征的实时行人检测预警系统(PDWS)。系统由检测模块和预警模块两部分组成, 其中检测模块使用Haar与HOG特征和AdaBoost与SVM分类器, 通过侧面行人样本库完成行人特征的提取与检测, 同时应用窗口拆分法与快速窗口扫描算法提高检测效率, 得到一个具有高检测率与低误检率的结果。在预警模块融合了行人距离、汽车速度及角速度信息, 判断前方行人存在碰撞的危险。使用系统及算法对城市环境复杂背景下横过街道的行人进行了实车验证。测试结果表明: 对704Pixel×576Pixel图像的检测帧率为13~18帧.s-1, 检测率大于85%, 误检率小于1%, 预警时间小于1s, 实车验证结果达到了车载主动安全系统实时性与准确性的要求。

     

  • 图  1  系统结构

    Figure  1.  System structure

    图  2  算法流程

    Figure  2.  Algorithm flow

    图  3  算法框架

    Figure  3.  Algorithm framework

    图  4  特征模板

    Figure  4.  Feature templates

    图  5  窗口拆分法原理

    Figure  5.  Schematic of window-dividing method

    图  6  OCS算法原理

    Figure  6.  Schematic of OCS algorithm

    图  7  直线行驶

    Figure  7.  Straight driving

    图  8  转弯

    Figure  8.  Turning

    图  9  行人检测系统构架

    Figure  9.  Architecture of pedestrian detection system

    图  10  测试系统

    Figure  10.  Test system

    图  11  安装结构与预警系统

    Figure  11.  Installation structure and PDWS

    图  12  试验模型

    Figure  12.  Experimental model

    图  13  弱分类器数

    Figure  13.  Numbers of weak classifiers

    图  14  否决率累计量

    Figure  14.  Accumulative reject rates

    图  15  DET曲线对比

    Figure  15.  Comparison of DET curves

    图  16  两阶段的检测结果

    Figure  16.  Test results of 2 steps

    图  17  数目不同的行人检测效果

    Figure  17.  Detection effects with different numbers of pedestrians

    图  18  不同距离下的检测效果

    Figure  18.  Detection effects at different distances

    表  1  检测结果

    Table  1.   Detection result

    视频 方法 检测率% 误检率% 帧率/(帧·s-1)
    1 HOG+SVM 100.0 1.70 0.75
    定位+验证 96.7 0.00 8.30
    OCS 93.3 1.70 11.80
    OCS+Div2 91.7 0.00 14.60
    2 OCS+Div3 80.0 0.00 17.20
    HOG+SVM 92.5 3.75 0.75
    定位+验证 90.0 0.00 8.60
    OCS 86.3 0.00 12.30
    3 OCS+Div2 82.5 0.00 14.80
    OCS+Div3 86.3 0.00 18.50
    HOG+SVM 95.8 0.00 0.75
    定位+验证 90.1 4.70 8.70
    4 OCS 93.0 4.70 12.10
    OCS+Div2 85.2 0.00 14.20
    OCS+Div3 73.2 0.00 17.80
    HOG+SVM 97.4 4.00 0.75
    定位+验证 94.1 0.00 7.90
    OCS 88.9 0.00 11.20
    OCS+Div2 83.0 2.00 13.20
    OCS+Div3 71.2 0.00 16.30
    下载: 导出CSV

    表  2  不同距离下的检测率

    Table  2.   Detection rates at different distances

    距离/m 5 10 20 30 40 50 60
    检测率/% 78.0 83.0 87.5 80.0 50.0 29.0 < 10.0
    下载: 导出CSV
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  • 收稿日期:  2012-05-28

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