基于多模板的深度核相关滤波跟踪

李雪晴; 杨德东; 毛宁; 杨福才

doi:10.3788/YJYXS20173212.0993

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基于多模板的深度核相关滤波跟踪

图像处理 | 更新时间：2020-07-07

- 基于多模板的深度核相关滤波跟踪
- Depth kernel correlation filtering tracking based on multi-template
- 液晶与显示 2017年32卷第12期页码：993-998
- 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：
  
  [ "李雪晴(1994-), 女, 安徽铜陵人, 硕士研究生, 2016年于天津农学院获得学士学位, 主要从事图形处理、目标跟踪方面的研究。E-mail:lixueq1208@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, 现为北京航空航天大学博士后, 主要从事智能感知与控制、目标跟踪方面的研究。E-mail:ydd12677@163.com" ]
- 基金信息：
  
  国家自然科学基金项目(61203076);河北省自然科学基金项目(F2017202009);National Natural Science Foundation of China(61203076);the Natural Science Foundation of Hebei, China(F2017202009)
- DOI：10.3788/YJYXS20173212.0993
  中图分类号： TP391
- 收稿日期：2017-08-01，
  
  录用日期：2017-9-6，
  
  纸质出版日期：2017-12-05
- 稿件说明：
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李雪晴, 杨德东, 毛宁, 等. 基于多模板的深度核相关滤波跟踪[J]. 液晶与显示, 2017,32(12):993-998.

Xue-qing LI, De-dong YANG, Ning MAO, et al. Depth kernel correlation filtering tracking based on multi-template[J]. Chinese journal of liquid crystals and displays, 2017, 32(12): 993-998.
李雪晴, 杨德东, 毛宁, 等. 基于多模板的深度核相关滤波跟踪[J]. 液晶与显示, 2017,32(12):993-998. DOI： 10.3788/YJYXS20173212.0993.

Xue-qing LI, De-dong YANG, Ning MAO, et al. Depth kernel correlation filtering tracking based on multi-template[J]. Chinese journal of liquid crystals and displays, 2017, 32(12): 993-998. DOI： 10.3788/YJYXS20173212.0993.

摘要

针对跟踪过程中出现的遮挡、尺度变化、光照变化等问题，文章基于多模板提出深度核相关滤波算法。首先，多模板算法选取最佳滤波参数优化分类器训练样本的能力，多特征算法利用多种特征优化目标外观模型提高了跟踪过程的鲁棒性；其次，利用深度图信息计算跟踪过程中目标重叠率，判断目标的遮挡情况，遮挡时重新定义目标搜索区域，并判断是否重新跟踪目标，降低遮挡情况下的算法漂移问题；最后，根据目标遮挡情况判断是否更新分类器参数和目标外观模型，提高模板更新的可靠性。利用Princeton数据库测试算法，成功率和精度分别达到85.1和98.6，比第二名算法分别提高了7.04%和4.67%。实验从成功率、精确度方面说明基于多模板的深度核相关滤波算法优于传统算法，有一定研究价值。

Abstract

Aiming at the problems of occlusion

scale transformation and illumination change in the tracking process

a depth kernel correlation filtering algorithm is proposed based on the multi-template. Firstly

the multi-template algorithm selects the best filtering parameters to optimize the ability of the classifier training samples. The various features are used to optimize the target appearance model which improves the robustness of the multi-feature algorithm. Then

in the tracking process

the depth map information is applied to calculate the target overlap rate which is employed to judge whether the tracking target is occluded. When occlusion occurs

the target search area is redefined

and the target is judge whether to track gain. So it reduces the problem of algorithm drift in the case of occlusion. Finally

according to whether or not occlusion occurs

the classifier parameters and target appearance model are determined whether to update. It improves the reliability of template updates. Using the Princeton database test algorithm

the success rate and accuracy is 85.1% and 98.6% respectively

which is 7.04% and 4.67% higher than the second algorithm respectively. Experiments show that the depth kernel correlation filtering algorithm based on multi-template is superior to the traditional algorithm

and has certain research value.

关键词

Keywords

references

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