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The visual tracking system using a stereo vision robot

机译:使用立体视觉机器人的视觉跟踪系统

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摘要

Purpose - The purpose of this paper is to use vision stereo to simultaneously acquire image pairs under a normal environment. Then the methods of moving edges detection and moving target shifting are applied to reduce noise error in order to position a target efficiently. The target is then double confirmed via image merge and alignment. After positioning, the visual difference between the target and the image created by the stereo vision system is measured for alignment. Finally, the image depth of the target is calculated followed by real-time target tracking. Design/methodology/approach - This study mainly applies Sobel image principle. In addition, moving edges detection and moving target shifting are also used to work with system multi-threading for improving image identification efficiency. Findings - The results of the experiment suggest that real-time image tracking and positioning under a pre-set environment can be effectively improved. On the other hand, tracking and positioning are slightly affected under a normal environment. Errors of distance measurements occur because there is more noise existing. Research limitations/implications - This study mainly determines the movements and positioning of an object or a target via image. However, the stability of moving edges detection executed by the stereo vision system can be affected if the light sources in an environment are too strong or extreme. Practical implications - So far the method of tracking and positioning a moving object has been applied to surveillance systems or the application which requires measuring and positioning under a normal environment. The method proposed by this study can also be used to construct a 3D environment. Originality/value - The method proposed by this study can also be used to construct a 3D environment or tracking moving object to measure the distance.
机译:目的-本文的目的是在正常环境下使用视觉立体图同时获取图像对。然后应用移动边缘检测和移动目标移动的方法来减少噪声误差,以便有效地定位目标。然后通过图像合并和对齐对目标进行两次确认。定位后,将测量目标与由立体视觉系统创建的图像之间的视觉差异,以进行对齐。最后,计算目标的图像深度,然后进行实时目标跟踪。设计/方法/方法-本研究主要应用Sobel图像原理。此外,移动边缘检测和移动目标移动也可与系统多线程配合使用,以提高图像识别效率。发现-实验结果表明,可以有效地改善预设环境下的实时图像跟踪和定位。另一方面,在正常环境下跟踪和定位会受到轻微影响。由于存在更多的噪声,因此会发生距离测量的错误。研究局限性/含义-该研究主要通过图像确定对象或目标的移动和位置。但是,如果环境中的光源太强或太强,则立体视觉系统执行的移动边缘检测的稳定性可能会受到影响。实际意义-到目前为止,跟踪和定位移动物体的方法已应用于监视系统或需要在正常环境下进行测量和定位的应用程序。这项研究提出的方法也可以用于构建3D环境。原创性/价值-这项研究提出的方法也可以用于构建3D环境或跟踪移动物体以测量距离。

著录项

  • 来源
    《Engineering Computations》 |2014年第8期|1790-1799|共10页
  • 作者单位

    Department of Mechanical Engineering, Tatung University, Taipei, Taiwan;

    Department of Mechanical Engineering, Tatung University, Taipei, Taiwan;

    Department of Applied Geoinformatics,Chia Nan University of Pharmacy and Science, Tainan City, Taiwan;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Image tracking system; Stereo vision;

    机译:图像跟踪系统;立体视觉;

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