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Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

机译：散货港口无人监视区域的快速人为检测算法研究

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摘要

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.
机译：随着港口自动化的发展，大多数利用重型设备的操作领域逐渐变得无人驾驶。因此，必须以有效和实时的方式监视这些字段。本文提出了一种基于图像处理的快速人体检测算法。为了加快检测过程，使用了可避免对原始HOG进行大量重复计算并忽略不重要特征的优化的定向梯度直方图（HOG）算法来实时描述人体轮廓。基于HOG功能，使用由散装端口的场景图像组成的训练样本集，对与AdaBoost分类器结合的支持向量机（SVM）分类器进行训练，以检测人。最后，在天津港进行的人体检测实验结果表明，所提算法的准确性与传统算法大致相同，所提算法仅耗时1/7。验证了所提出的快速人类检测算法的准确性和计算时间，以满足无人港口区域的安全要求。

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  • 来源
    《Mathematical Problems in Engineering》 |2014年第21期|386764.1-386764.17|共17页
  • 作者

    Mi Chao; He Xin; Liu Haiwei; Huang Youfang; Mi Weijian;

  • 作者单位

    Shanghai Maritime Univ, Container Supply Chain Tech Engn Res Ctr, Shanghai 201306, Peoples R China.;

    Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China.;

    Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China.;

    Shanghai Maritime Univ, Container Supply Chain Tech Engn Res Ctr, Shanghai 201306, Peoples R China.;

    Shanghai Maritime Univ, Container Supply Chain Tech Engn Res Ctr, Shanghai 201306, Peoples R China.;

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