Color and edge histogram based multi-target tracking

ZHANG Lei; WANG Yan-jie; SUN Hong-hai; YAO Zhi-jun; HE Shu-wen

doi:10.3788/YJYXS20163106.0595

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Color and edge histogram based multi-target tracking

Display and Image | 更新时间：2020-08-12

- Color and edge histogram based multi-target tracking
- Chinese Journal of Liquid Crystals and Displays Vol. 31, Issue 6, Pages: 595-603(2016)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,130033
  2. 中国科学院大学, 北京 100049
  3. 北方自动控制技术研究所,山西太原,030006
- 作者简介：
- 基金信息：
  
  Supported by National High Technology Research and Development Program of China(No.2
- DOI：10.3788/YJYXS20163106.0595
  CLC： TP391
- Received：22 July 2015，
  
  Revised：23 August 2015，
  
  Published：05 June 2016
- 稿件说明：
移动端阅览
张雷, 王延杰, 孙宏海等. 基于颜色和边缘直方图的多目标跟踪[J]. 液晶与显示, 2016,31(6): 595-603

ZHANG Lei, WANG Yan-jie, SUN Hong-hai etc. Color and edge histogram based multi-target tracking[J]. , 2016,31(6): 595-603
张雷, 王延杰, 孙宏海等. 基于颜色和边缘直方图的多目标跟踪[J]. 液晶与显示, 2016,31(6): 595-603 DOI： 10.3788/YJYXS20163106.0595.

ZHANG Lei, WANG Yan-jie, SUN Hong-hai etc. Color and edge histogram based multi-target tracking[J]. , 2016,31(6): 595-603 DOI： 10.3788/YJYXS20163106.0595.

摘要

为了解决基于颜色直方图的多目标跟踪方法对复杂场景适应能力差

容易丢失目标的问题

文中提出一种将颜色直方图与边缘方向直方图相结合的多目标跟踪方法。该方法首先采用一种分块连通域标记方法进行多目标提取

并获得目标的颜色、边缘特征;然后融合目标颜色与边缘两种特征来描述目标的外观模型;最后对跟踪过程中的目标模板进行更新。实验结果表明

该方法对于目标在尺度、光照、姿态发生变化以及目标发生旋转情况下能够实现目标的稳定跟踪

具有很强的鲁棒性。实验中对3组挑战性的视频序列进行了测试

目标数目选定为2个

目标窗口大小为64 pixels 64 pixels的情况下

本文方法跟踪速度最高可达20 fps

基本上可以满足实时性的跟踪需求。

Abstract

In order to solve the problem that the color-histogram based multi-target tracking is poorly adaptive to the complex scene and easy to lose the target

a multi-target tracking which combines the color-histogram and edge-direction-histogram is proposed in this paper. Firstly

the algorithm adopts a cell-based connected component labeling algorithm to extract the multiple targets and obtains the color and edge features of the targets. Then we combine the color and edge features to describe the target appearance models. Finally

the target models are updated in the tracking process. The experimental results show that the algorithm can complete the stable tracking when the target undergoes the scale variation

illumination changes

pose variation and rotation. And the algorithm has a strong robustness. We perform our algorithm on 3 challenging video sequences in the experiment

when the targets number is chosen to 2 and the target window size is 64 pixels64 pixels

the maximum speed of our approach can reach 20 fps and it can mainly satisfy the real-time tracking requirement.

关键词

Keywords

references

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