多特征融合的多模板协同相关跟踪

毛宁; 杨德东; 杨福才; 蔡玉柱

doi:10.3788/YJYXS20173202.0153

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多特征融合的多模板协同相关跟踪

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- 多特征融合的多模板协同相关跟踪
- Multi-template collaborative correlation tracking with multi-feature fusion
- 液晶与显示 2017年32卷第2期页码：153-162
- 作者机构：
  
  河北工业大学控制科学与工程学院, 天津 300130
- 作者简介：
  
  [ "毛宁(1992-), 男, 河南周口人, 硕士研究生, 2015年于河南城建学院获得学士学位, 主要从事机器学习、目标跟踪方面的研究。E-mail:maon316@163.com" ]
  杨德东(1977-), 男, 辽宁阜新人, 副教授, 硕士生导师, 2000年于大连铁道学院获得学士学位, 2003年于大连铁道学院获得硕士学位, 2007年于东北大学获得博士学位, 主要从事智能感知与控制、目标检测与跟踪等方面的研究。E-mail:ydd12677@163.com
- 基金信息：
  
  国家自然科学基金项目(61203076);天津市自然科学基金项目(13JCQNJC03500);National Natural Science Foundation of China(61203076);Natural Science Foundation of Tianjin, China(13JCQNJC03500)
- DOI：10.3788/YJYXS20173202.0153
  中图分类号： TP391
- 收稿日期：2016-09-23，
  
  录用日期：2016-10-19，
  
  纸质出版日期：2017-02-05
- 稿件说明：
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毛宁, 杨德东, 杨福才, 等. 多特征融合的多模板协同相关跟踪[J]. 液晶与显示, 2017,32(2):153-162.

Ning MAO, De-dong YANG, Fu-cai YANG, et al. Multi-template collaborative correlation tracking with multi-feature fusion[J]. Chinese journal of liquid crystals and displays, 2017, 32(2): 153-162.
毛宁, 杨德东, 杨福才, 等. 多特征融合的多模板协同相关跟踪[J]. 液晶与显示, 2017,32(2):153-162. DOI： 10.3788/YJYXS20173202.0153.

Ning MAO, De-dong YANG, Fu-cai YANG, et al. Multi-template collaborative correlation tracking with multi-feature fusion[J]. Chinese journal of liquid crystals and displays, 2017, 32(2): 153-162. DOI： 10.3788/YJYXS20173202.0153.

摘要

针对目标跟踪中的目标尺度变化、遮挡、光照变化、相似目标混淆等问题，本文提出多特征融合的协同相关跟踪算法。首先，本文用多种特征构建目标外观模型，提高目标模型的鲁棒性，增强跟踪的抗形变能力和抗光照变化能力。然后，利用定点优化策略，解决多模板滤波优化问题，获得最佳滤波参数，通过多模板相关滤波算法估计目标位置，利用改进的尺度池方法解决目标尺度变化问题。最后，利用目标置信度判别跟踪目标是否发生遮挡，当目标发生遮挡时，利用CUR滤波模块重新检测目标，解决遮挡情况下跟踪任务。本文利用OTB-2013数据集中的方法测试本文算法，实验表明本文算法的整体成功率和精确度为0.622和0.830，本文算法在目标发生尺度变化、遮挡、光照变化、相似目标混淆等问题情况下，能准确、可靠地跟踪目标，具有一定研究价值。

Abstract

In order to solve the problem of target scale change

occlusion

illumination change

similar target confusion in target tracking

the paper proposes multi-template collaborative correlation tracking by multi-feature fusion algorithm. First

this paper constructs the target appearance model with various features

improves the robustness of the target model

and enhances the ability of anti deformation and the ability of light variation. Then

the fixed-point optimization strategy is used to solve the multi template filtering optimization problem

the optimal filter parameters are obtained and the target position is estimated by the multi-template correlation filtering algorithm. Target scale change is solved by the improved scale pool method. Finally

the target confidence is used to judge whether the tracking target is occluded. When the target is occluded

the CUR filter module is used to re-detect the target. The tracking task in the occlusion case is resolved. In this paper

we use the method of OTB-2013 dataset to test the algorithm. Experimental results show that the overall success rate and accuracy of the algorithm are 0.622 and 0.830.Under the situation of occlusion

scale change

similar target interfere and illumination variation

the algorithm can accurately and reliably track the target which has certain research value.

关键词

Keywords

references

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