核相关滤波跟踪算法的尺度自适应改进

doi:10.11772/j.issn.1001-9081.2017.03.811

计算机应用 ›› 2017, Vol. 37 ›› Issue (3): 811-816.DOI: 10.11772/j.issn.1001-9081.2017.03.811

• 计算机视觉与虚拟现实 • 上一篇下一篇

核相关滤波跟踪算法的尺度自适应改进

钱堂慧, 罗志清, 李果家, 李应芸, 李显凯

昆明理工大学国土资源工程学院, 昆明 650093

收稿日期:2016-09-26 修回日期:2016-10-21 发布日期:2017-03-22 出版日期:2017-03-10
通讯作者: 罗志清
作者简介:钱堂慧(1989-),男,云南曲靖人,硕士研究生,主要研究方向:目标跟踪;罗志清(1963-),男,云南玉溪人,副教授,硕士,主要研究方向:数字化测图、GIS;李果家(1992-),女,云南昆明人,硕士研究生,主要研究方向:视觉跟踪;李应芸(1991-),女,云南保山人,硕士研究生,主要研究方向:烟雾识别;李显凯(1992-),男,云南楚雄人,硕士研究生,主要研究方向:运动目标检测。

Scale adaptive improvement of kernel correlation filter tracking algorithm

QIAN Tanghui, LUO Zhiqing, LI Guojia, LI Yingyun, LI Xiankai

College of Land and Resources Engineering, Kunming University of Science and Technology, Kunming Yunnan 650093, China

Received:2016-09-26 Revised:2016-10-21 Online:2017-03-22 Published:2017-03-10

摘要/Abstract

摘要： 针对基于检测的核相关滤波跟踪（CSK）算法难以适应目标尺度变化的问题，提出多尺度核相关滤波分类器以实现尺度自适应目标跟踪。首先，采用多尺度图像构建样本集，训练多尺度核相关滤波分类器，通过分类器对目标的尺度估计实现目标的最佳尺度检测；然后，在最佳尺度下采集样本在线学习更新分类器，实现尺度自适应的目标跟踪。对比实验与分析表明，本文算法在目标跟踪过程中能够正确适应目标的尺度变化，相比CSK算法，偏心距误差减少至其1/5~1/3，能满足复杂场景长时间跟踪的需求。

关键词: 目标跟踪, 多尺度图像, 自适应, 核相关滤波

Abstract: To solve the problem that Circulant Structure of tracking-by-detection with Kernels (CSK) is difficult to adapt to the target scale change, a multi-scale kernel correlation filter classifier was proposed to realize the scale adaptive target tracking. Firstly, the multi-scale image was used to construct the sample set, the multi-scale kernel correlation filtering classifier was trained by the sample set, for target size estimation to achieve the goal of the optimal scale detection, and then the samples collected on the optimal target scale were used to update the classifier on-line to achieve the scale-adaptive target tracking. The comparative experiments and analysis illustrate that the proposed algorithm can adapt to the scale change of the target in the tracking process, the error of the eccentricity is reduced to 1/5 to 1/3 that of CSK algorithm, which can meet the needs of long time tracking in complex scenes.

Key words: target tracking, multi-scale image, self-adaption, Kernel Correlation Filter (KCF)

中图分类号:

TP391.41

钱堂慧, 罗志清, 李果家, 李应芸, 李显凯. 核相关滤波跟踪算法的尺度自适应改进[J]. 计算机应用, 2017, 37(3): 811-816.

QIAN Tanghui, LUO Zhiqing, LI Guojia, LI Yingyun, LI Xiankai. Scale adaptive improvement of kernel correlation filter tracking algorithm[J]. Journal of Computer Applications, 2017, 37(3): 811-816.

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核相关滤波跟踪算法的尺度自适应改进

Scale adaptive improvement of kernel correlation filter tracking algorithm

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