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山东大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (5): 14-19.doi: 10.6040/j.issn.1672-3961.0.2014.090

• 控制科学与工程 • 上一篇    下一篇

基于局部敏感直方图的稀疏表达跟踪算法

葛凯蓉, 常发亮, 董文会   

  1. 山东大学控制科学与工程学院, 山东 济南 250061
  • 收稿日期:2014-03-27 修回日期:2014-09-24 发布日期:2014-03-27
  • 通讯作者: 常发亮(1965-), 男, 山东潍坊人, 博士, 教授, 博士生导师, 主要研究方向为机器视觉和模式识别.E-mail:flchang@sdu.edu.cn E-mail:flchang@sdu.edu.cn
  • 作者简介:葛凯蓉(1988-), 女, 山东烟台人, 硕士研究生, 主要研究方向为机器视觉及运动目标跟踪.E-mail:gekairong303@163.com
  • 基金资助:
    国家自然科学基金项目(61273277);高等学校博士学科点专项科研基金资助课题(2013013111003);教育部留学回国人员科研启动基金资助项目(20101174);山东省自然科学基金(ZR2011FM032)

Sparse representation tracking method based on locality sensitive histogram

GE Kairong, CHANG Faliang, DONG Wenhui   

  1. School of Control Science and Engineering, Shandong University, Jinan 250061, China
  • Received:2014-03-27 Revised:2014-09-24 Published:2014-03-27

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摘要： 为解决目标跟踪过程中光照变化、姿态变化等问题,提出了一种基于局部敏感直方图特征的稀疏表达跟踪方法。对粒子滤波获取的多个候选目标提取局部敏感直方图特征,并根据模板字典,采用改进的L1范数模型求取每个候选目标的稀疏表示系数;然后计算每个候选目标的权重,选取权重最大的候选目标作为跟踪结果。实验结果表明,本算法能很好实现对目标的跟踪,在解决光照变化、姿态变化等问题方面有较好的效果。

关键词: 局部敏感直方图, 稀疏表达, 粒子滤波, 目标跟踪, 鲁棒性

Abstract: In order to solve the problems of illumination and pose change during target tracking, a sparse representation tracking method based on local sensitive histogram was proposed. Local sensitive histogram features of multiple candidate targets were extracted, and sparse representation coefficient of each candidate target was calculated based on template dictionary by using modified L1 norm model. Then, the weight of each candidate target was calculated. The candidate target which had the largest weight was selected as tracking result. Experimental results demonstrated that the method can track the target accurately and effectively and has advantage in illumination and pose change.

Key words: local sensitive histogram, particle filter, target tracking, robust, sparse representation

中图分类号: 

  • TP391.4
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