基于双目标模型的时空上下文跟踪算法

张红颖; 郑轩

doi:10.3788/OPE.20162405.1215

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基于双目标模型的时空上下文跟踪算法

信息科学 | 更新时间：2020-08-13

- 基于双目标模型的时空上下文跟踪算法
- Spatio-temporal context tracking algorithm based on dual-object model
- 光学精密工程 2016年24卷第5期页码：1215-1223
- 作者机构：
  
  中国民航大学电子信息与自动化学院天津,300300
- 作者简介：
- 基金信息：
  
  国家自然科学基金民航联合基金项目(No.U1333105)();天津市自然科学基金青年基金资助项目(No.12JCQNJC00600)();中央高校基本科研业务费专项资
- DOI：10.3788/OPE.20162405.1215
  中图分类号： TP391.4
- 收稿日期：2016-01-08，
  
  修回日期：2016-03-04，
  
  纸质出版日期：2016-05-25
- 稿件说明：
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张红颖, 郑轩,. 基于双目标模型的时空上下文跟踪算法[J]. 光学精密工程, 2016,24(5): 1215-1223

ZHANG Hong-ying, ZHENG Xuan,. Spatio-temporal context tracking algorithm based on dual-object model[J]. Editorial Office of Optics and Precision Engineering, 2016,24(5): 1215-1223
张红颖, 郑轩,. 基于双目标模型的时空上下文跟踪算法[J]. 光学精密工程, 2016,24(5): 1215-1223 DOI： 10.3788/OPE.20162405.1215.

ZHANG Hong-ying, ZHENG Xuan,. Spatio-temporal context tracking algorithm based on dual-object model[J]. Editorial Office of Optics and Precision Engineering, 2016,24(5): 1215-1223 DOI： 10.3788/OPE.20162405.1215.

摘要

传统的时空上下文跟踪算法在更新目标模型时不考虑跟踪结果的有效性

故目标被长时间遮挡后

目标模型容易被错误更新且难以修正。因此

本文提出了一种基于双目标模型的改进时空上下文跟踪算法以解决错误更新问题。该算法引入一个辅助目标判别模型来评估时空上下文算法跟踪结果的有效性

并根据评估结果对目标模型进行更新。辅助模型使用目标的局部纹理信息而不是相关性信息作为特征

在目标被长时间遮挡后也能准确评估更新内容的有效性

并能在遮挡结束后修正错误更新的目标模型。在多组数据集上的实验表明

改进算法在测试数据集上的跟踪成功率为82%

中心偏差为8 pixels;在长时间遮挡等干扰情况下的跟踪精度比原时空上下文算法有明显提升

实现了目标的可靠跟踪。

Abstract

The original spatio-temporal context (STC) tracking algorithm does not take the tracking results into account when it updates an object model

so the object model is wrongly updated and hard to be recovered after long term occlusion. To solve this problem

an improved spatio-temporal context tracking algorithm based on a dual-object model is proposed in this paper. In this method

an auxiliary object model is introduced to evaluate the effectiveness of original STC algorithm

and to update the object model based on the tracking results accordingly. By using texture information rather than correlation information as the characteristics

the auxiliary object model evaluates exactly the updated contents and corrects wrong updates after long-term occlusion. Experimental results on several groups dada sets indicate that the success rate of the proposed algorithm is 82%

and the center location error is 8 pixels. It implements the stable tracking and the tracking precision is superior to that of the original STC algorithm in complex scenes

especially after long-term occlusions.

关键词

Keywords

references

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