大理大学学报 ›› 2022, Vol. 7 ›› Issue (6): 42-47.

• 物理学 • 上一篇    下一篇

基于机器视觉的自动目标跟随系统设计与实现

  

  1. 1.大理大学工程学院,云南大理 671003;2.云南民族大学电气信息工程学院,昆明 650500
  • 收稿日期:2021-07-13 修回日期:2021-11-11 出版日期:2022-06-15 发布日期:2022-07-04
  • 作者简介:赵恩铭,副教授,博士,主要从事电子信息、信号处理等研究。
  • 基金资助:

    国家自然科学基金项目(6206500161761048);云南省地方本科高校(部分)基础研究联合专项资金项目 (2019FH001-066

Design and Implementation of Automatic Target Tracking System Based on Machine Vision

  1. 1.College of Engineering, Dali University, Dali, Yunnan 671003,China ; 2. School of Electrical Information Engineering, Yunnan Minzu University, Kunming 650500,China
  • Received:2021-07-13 Revised:2021-11-11 Online:2022-06-15 Published:2022-07-04

摘要: 为提高智能小车跟随目标时的准确性与实时性,设计了基于改进的April Tag算法为核心的自动跟随系统。针对当前目标跟踪算法实时性较低以及目标较小时识别效果不佳的问题,提出了基于April Tag算法的改进方案,当标签较大时在不影响识别准确率的前提下,通过降低图像质量的方法提高实时性;当标签过小时,通过提升图像质量并缩小检测范围的方法提高识别准确性。实验结果表明,改进后的April Tag算法具有更好的准确率与识别速度,有效提升了追踪目标时的实时性与准确率,满足智能跟随机器人对目标识别和跟随的要求。

关键词: STM32, 智能小车, April Tag算法, OpenMV

Abstract:

In order to improve the accuracy and real-time performance of the intelligent vehicle following the target an automatic following system based on the improved April Tag algorithm is designed. Aiming at the problems of low real-time performance of current target tracking algorithm and poor recognition performance when the target is small an improved scheme based on April Tag algorithm is proposed. The real-time performance is improved by reducing the image quality when the label is large while the recognition accuracy is not affected. The recognition accuracy is improved by improving the image quality and reducing the detection range when the label is too small. The experimental results show that the improved April Tag algorithm has better accuracy and recognition speed and improves the real-time performance and accuracy of target tracking effectively which meets the requirements of target recognition and tracking for intelligent following robot.

Key words: STM32, mart car, April Tag algorithm, OpenMV

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