吉林大学学报(信息科学版) ›› 2016, Vol. 34 ›› Issue (1): 86-71.

• 论文 • 上一篇    下一篇

基于可判别叶子的霍夫森林目标跟踪算法

鲁奉军, 王世刚, 赵文婷, 赵晓琳   

  1. 吉林大学通信工程学院, 长春130012
  • 收稿日期:2014-12-01 出版日期:2016-01-25 发布日期:2016-05-10
  • 作者简介:鲁奉军(1991—), 男, 吉林德惠人, 吉林大学硕士研究生, 主要从事目标跟踪与动作识别研究, (Tel)86-15948353221 (E-mail)lufengjun2012@ sina. com; 王世刚(1961—), 男, 长春人, 吉林大学教授, 博士生导师, 主要从事图像与视频信号智能处理研究, (Tel)86-431-85151539(E-mail)wangshigang@ vip. sina. com 。
  • 基金资助:

    吉林大学博士学科点基金资助项目(20120061110091)

Discriminative Leaf Based Hough Forest for Target Tracking

LU Fengjun, WANG Shigang, ZHAO Wenting, ZHAO Xiaolin   

  1. College of Communication Engineering, Jilin University, Changchun 130012, China
  • Received:2014-12-01 Online:2016-01-25 Published:2016-05-10

摘要:

为解决霍夫森林叶子投票引入的噪声信息, 提出一种基于可判别叶子的霍夫森林目标跟踪算法。在原始霍夫森林训练阶段, 叶子结点不具有判别能力, 不能分类图像斑块属于目标还是背景。含有背景的图像斑块包括大量的噪声, 而且在每个像素位置处, 收集图像斑块的表决信息耗费大量时间。实验结果表明, 该方法不仅能减少噪声表决信息, 还能增加目标检测的有效性。

关键词: 目标跟踪, 霍夫森林, 可判别叶子

Abstract:

In order to solve the noise information, which the leaf nodes votes in Hough Forest, this paper proposes an target tracking algorithm of the discriminative leaf based on Hough Forest. In the training phase of the original Hough Forest, leaf nodes do not have the ability to classify whether the image patches are target or background. The image patches contain a lot of noise, and aggregating voting information from all image patches extracted at each pixel location is really time-consuming. The experimental results demonstrate that the approach can reduce noise votes and increase tracking efficiency.

Key words: target detection, hough forest, discriminative leaf

中图分类号: 

  • TP391. 4