文献知网节
  • 记笔记
摘要:The technology of detecting and identifying multitarget is of significance for robotic visual navigation within the unknown, unstructured, complex scenes, whose result could be considered as important references of 3 D map building and path planning for mobile robot. Traditional algorithms of target detection can be applied to 2 D images without depth information generally, which is disturbed by few factors such as illumination, view and scale easily. Therefore, an approach on visual detecting multi-target of unstructured and complex scenes based on RGBD images is proposed in this article to solve the above problem, which is composed of extracting descriptor of rotation and scale invariance feature, local encoding of targets, random ferns classifier training, Hough map generation, Hough voting theoretical model and local maximum search. Experimental results have shown that the proposed approach reduce the calculation of extracting and matching local feature, improve the accuracy of object recognition and detection in unknown complex environments, be capable of well robust against few disturbing factors i.e. rotation, scale, illumination, occlusion and non-rigid body deformation.
会议名称:

5th International Conference on Automation, Control and Robotics Engineering (CACRE 2020)

会议时间:

2020-09-19

会议地点:

中国辽宁大连

  • 专辑:

    电子技术及信息科学

  • 专题:

    计算机软件及计算机应用; 自动化技术

  • DOI:

    10.26914/c.cnkihy.2020.032793

  • 分类号:

    TP242;TP391.41

  • 手机阅读
    即刻使用手机阅读
    第一步

    扫描二维码下载

    "移动知网-全球学术快报"客户端

    第二步

    打开“全球学术快报”

    点击首页左上角的扫描图标

    第三步

    扫描二维码

    手机同步阅读本篇文献

  • CAJ下载
  • PDF下载

下载手机APP用APP扫此码同步阅读该篇文章

下载:1 页码:616-621 页数:6 大小:772k

相关推荐
  • 相似文献
  • 读者推荐
  • 相关基金文献
  • 关联作者
  • 相关视频