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  <front>

    <journal-meta>

      <journal-title>Information Technology Journal</journal-title>

      <issn pub-type="ppub">1812-5638</issn>

      <issn pub-type="epub">1812-5646</issn>

      <publisher>

        <publisher-name>Asian Network for Scientific Information</publisher-name>

      </publisher>

    </journal-meta>


    <article-meta>

      <article-id pub-id-type="doi">10.3923/itj.2014.2611.2618</article-id>


      <title-group>

        <article-title><![CDATA[Study of Moving Obstacle Detection at Railway Crossing by Machine Vision]]></article-title>

      </title-group>


      <contrib-group>

        <contrib contrib-type="author" xlink:type="simple">


          <name name-style="western">

            <surname>Pu</surname>

            <given-names>Yong-Ren</given-names>

          </name>


          <name name-style="western">

            <surname>Chen</surname>

            <given-names>Li-Wei</given-names>

          </name>


          <name name-style="western">

            <surname>Lee</surname>

            <given-names>Su-Hsing</given-names>

          </name>


        </contrib>

      </contrib-group>


      <pub-date pub-type="collection">




        <month>12</month>


        <year>2014</year>

      </pub-date>


      <volume>13</volume>

      <issue>16</issue>


      <abstract><![CDATA[<p>This study is designed to develop an advanced safety system 
  that is able to detect the existence of moving obstacles at a railway crossing. 
  In a miniature railway crossing, scaled 1:22.5, the authors installed a grayscale 
  CCD camera and developed a graphical user interface to process the images of 
  the crossing. To achieve this goal, the software was programmed to perform several 
  image processing techniques such as image subtraction, binarization, morphological 
  transformation and segmentation to track down the moving obstacles. In addition, 
  portions of the monitored image around the rails were labeled as alert and alarm 
  zones where detected obstacles would trigger the sirens of this system. Under 
  various lighting conditions for the model cars with different colors in the 
  indoor environment, the experiments on the developed system demonstrated that 
  the level of illuminance was a significant factor affecting the average alert 
  accuracy rate but the color of cars was not. Overall, the average alert accuracy 
  rate reached 97.8%. The promising results of the system&#146;s capability to recognize 
  obstacles that might pose threats, merit pursuit of full-scale development at 
  railway crossing sites to provide effective protection against previously undetected, 
  now preventable incidents.</p>]]></abstract>


    </article-meta>

  </front>


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