基于多模板回归加权均值漂移的人体目标跟踪

贾松敏; 文林风; 王丽佳

doi:10.3788/OPE.20162409.2339

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基于多模板回归加权均值漂移的人体目标跟踪

信息科学 | 更新时间：2020-07-07

- 基于多模板回归加权均值漂移的人体目标跟踪
- Person tracking based on multi-template regression weighted mean shift
- 光学精密工程 2016年24卷第9期页码：2339-2346
- 作者机构：
  
  1.北京工业大学信息学部, 北京 100124
  2.计算智能与智能系统北京市重点实验室, 北京 100124
  3.河北工业职业技术学院信息工程与自动化系, 河北石家庄 050091
- 作者简介：
  
  [ "贾松敏(1964-)，女，北京人，教授，博士生导师，2002年于日本国立电气通信大学获得博士学位，主要从事智能服务机器人及其关键性技术、机器人分散控制、计算机视觉等方面的研究。E-mail:jsm@bjut.edu.cn" ]
  文林风(1990-), 男, 湖南衡山人, 硕士研究生, 2013年于北京工业大学获得学士学位, 主要从事智能系统与模式识别等方面的研究。E-mail:wlwind@emails.bjut.edu.cn WEN Lin-feng, E-mail:wlwind@emails.bjut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61175087);北京工业大学智能机器人“大科研”推进计划“智能服务机器人模块化及实用化关键技术研究”资助项目
- DOI：10.3788/OPE.20162409.2339
  中图分类号： TP391.4
- 收稿日期：2016-04-11，
  
  录用日期：2016-6-20，
  
  纸质出版日期：2016-09
- 稿件说明：
移动端阅览
贾松敏, 文林风, 王丽佳. 基于多模板回归加权均值漂移的人体目标跟踪[J]. 光学精密工程, 2016,24(9):2339-2346.

Song-min JIA, Lin-feng WEN, Li-jia WANG. Person tracking based on multi-template regression weighted mean shift[J]. Optics and precision engineering, 2016, 24(9): 2339-2346.
贾松敏, 文林风, 王丽佳. 基于多模板回归加权均值漂移的人体目标跟踪[J]. 光学精密工程, 2016,24(9):2339-2346. DOI： 10.3788/OPE.20162409.2339.

Song-min JIA, Lin-feng WEN, Li-jia WANG. Person tracking based on multi-template regression weighted mean shift[J]. Optics and precision engineering, 2016, 24(9): 2339-2346. DOI： 10.3788/OPE.20162409.2339.

摘要

针对移动机器人跟踪人体目标时目标因角度大幅变化引起外观改变造成的跟踪无效，提出了多模板回归加权均值漂移跟踪方法。该方法通过建立目标的多模板模型，应用均值漂移算法实现目标跟踪。首先，根据前一帧均值漂移结果和当前帧头肩粗定位结果确定目标模板集，使其包含目标人体的位姿和角度改变。然后，采用多模板回归加权均值漂移实现目标的精确定位。在多模板均值漂移中引入回归模型实现颜色纹理特征与目标模型相似度之间的映射，从而控制模板数量，保证目标检测的实时性。最后，分别在视频图像和机器人目标跟踪平台上对所提方法进行实验验证。结果显示，图像处理平均时间为86.4 s/frame，满足机器人跟踪的实时性要求。该方法解决了目标特征在跟踪过程中发生变化的问题，提高了机器人跟踪时对目标人体特征变化的鲁棒性。

Abstract

To solve the invalid tracking of a human target caused by appearance variations due to large angle change of the target in a robot mobile tracking

a multi-template regression weighted mean-shift algorithm was proposed. The algorithm could implement the target tracking by building a multi-template model of the target and applying mean shift. Firstly

the template set was obtained according to the results from mean shift procedure of the last frame and the coarse location information of head-shoulder model of a current frame

by which the position and angle variation of the target person were included. Then

the multi-template regression weighted mean-shift algorithm was used to determine the precise location of the target person. The regression model was introduced to multi-template mean shift to implement a map from color-texture feature to the similarity of target model to limit the number of templates and to ensure the real-time performance of the target detection. Finally

the proposed algorithm was verified by videos and robot tracking tests. The results show that the image average treatment time is 86.4 ms/frame

which satisfies the requirement of person tracking for a mobile robot. The method solves the appearance variation problem of targets in tracking processing and improves the robustness of human targets to its feature variations.

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