Person tracking of a mobile robot using improved Mean Shift

WANG Li-jia1; 2; JIA Song-min1; WANG Shuang1; LI Xiu-zhi1

doi:10.3788/OPE.20132109.2364

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Person tracking of a mobile robot using improved Mean Shift

更新时间：2020-08-12

- Person tracking of a mobile robot using improved Mean Shift
- Optics and Precision Engineering Vol. 21, Issue 9, Pages: 2364-2370(2013)
- 作者机构：
  
  1. 河北工业职业技术学院信息工程与自动化系,河北石家庄,05000
  2. 北京工业大学电子信息与控制工程学院，北京 100124
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132109.2364
  CLC： TP242.6;TP391.4
- Received：12 March 2013，
  
  Revised：03 May 2013，
  
  Published Online：30 September 2013，
  
  Published：15 September 2013
- 稿件说明：
移动端阅览
王丽佳贾松敏王爽李秀智. 采用改进Mean Shift算法的移动机器人行人跟踪[J]. 光学精密工程, 2013,21(9): 2364-2370

WANG Li-jia JIA Song-min WANG Shuang LI Xiu-zhi. Person tracking of a mobile robot using improved Mean Shift[J]. Editorial Office of Optics and Precision Engineering, 2013,21(9): 2364-2370
王丽佳贾松敏王爽李秀智. 采用改进Mean Shift算法的移动机器人行人跟踪[J]. 光学精密工程, 2013,21(9): 2364-2370 DOI： 10.3788/OPE.20132109.2364.

WANG Li-jia JIA Song-min WANG Shuang LI Xiu-zhi. Person tracking of a mobile robot using improved Mean Shift[J]. Editorial Office of Optics and Precision Engineering, 2013,21(9): 2364-2370 DOI： 10.3788/OPE.20132109.2364.

摘要

为了提高移动机器人目标跟踪系统在复杂环境中的跟踪性能，提出在双层定位机制下采用基于自适应核函数的Mean Shift算法实现目标跟踪。利用射频识别器件（RFID）检测携带标签的目标，实现外层粗定位并确定感兴趣区域（ROI）；在内层则根据对视差图的ROI的处理结果确定初始搜索窗口，然后应用基于自适应核函数的Mean Shift算法在从立体相机获得的左图中应用基于自适应核函数的Mean Shift算法实现对目标的精确定位。自适应核函数由目标的区域特征与Epanechnikov函数相融合构成，克服了目标边缘处背景像素对目标颜色概率分布的影响。与传统的Mean Shift算法相比，所提方法在同色背景干扰下仍能准确跟踪目标。另外，RFID限定了图像搜索范围，节省了运算开支，图像处理的平均时间为62.11 ms/frame，满足实时跟踪的要求。实验结果表明，该方法可实现移动机器人在同色背景干扰、遮挡、目标快速移动等情况下的目标跟踪。

Abstract

To improve the performance of person tracking for a mobile robot in complex environments

an adaptive kernel function based Mean Shift algorithm was proposed by using a coarse to fine localization mechanism. In the outer layer

a Radio Frequency Identification Device (RFID) was adopted to detect the person with an ID tag to determine the Region of Interest (ROI) coarsely. In the inner layer

the ROI of a disparity image was processed to estimate an initial searching window. Then

the adaptive kernel based Mean Shift algorithm was applied to location of the person precisely in the left image from a stereo camera. The adaptive kernel function was combined with the regional feature of person and the Epanechnikov function

which can reduce the effect of the background pixel on the targets color probability distribution. Compared with the traditional Mean Shift algorithm

the presented algorithm can track the target successfully when the background has the same color. Furthermore

the searching area is narrowed by the RFID

so that the computational cost is reduced. The average computing time is 62.11 ms/frame

which satisfies the requirements of real-time target tracking. The experimental results indicate that the proposed tracking method can complete the target tracking in a background with the same color

short-term occlusion

fast moving

and a sudden turn for a mobile robot.

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references

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