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基于自适应的核相关滤波的目标跟踪算法

A Target Tracking Algorithm Based on Adaptive Kernelized Correlation Filtering

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摘要

针对核相关滤波器(KCF)跟踪算法在目标发生尺度变化和严重遮挡的情况下跟踪失败的问题, 提出了一种基于自适应的核相关滤波的目标跟踪算法。该算法运用了尺度估计策略, 使跟踪框自适应, 用多项式核函数来减少计算量, 采用了FHog目标特征代替原来的Hog特征, 获取更多的目标特征信息。实验采用OTB-2013评估基准的50组视频序列进行测试, 并与其他31种跟踪算法进行对比, 测试所提算法的有效性。实验结果表明: 所提算法成功率为0.549, 精确度为0.736, 排名第一, 与KCF算法相比, 分别提高了3.8%和1.0%。该算法在目标发生尺度变化、严重遮挡等复杂情况下, 均具有较强的稳健性和鲁棒性。

Abstract

In order to solve the problem of tracking failure of the Kernelized Correlation Filtering (KCF) tracking algorithm in the case of target scale changes and severe occlusion,an adaptive tracking algorithm is proposed based on kernelized correlation filtering.The algorithm uses a scale estimation strategy to adapt the tracking frame to target scale changes,and uses polynomial kernel functions to reduce the computational complexity.The FHog target feature is used to replace the original Hog feature to obtain more target feature information.In the experiment,50 sets of video sequences based on the OTB-2013 evaluation benchmark were tested and compared with other 31 tracking algorithms to verify the effectiveness of the proposed algorithm.The experimental results show that:the success rate of this algorithm is 0.549 and the accuracy is 0.736,ranking first,which is improved by 3.8% and 1.0% respectively compared with the KCF algorithm.The algorithm has strong steadiness and robustness under complex conditions such as target scale changes and severe occlusion.

Newport宣传-MKS新实验室计划
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中图分类号:TP391.41

DOI:10.3969/j.issn.1671-637x.2019.04.010

所属栏目:学术研究

收稿日期:2018-05-07

修改稿日期:2018-06-07

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李海彪:四川大学, a.电气信息学院
黄 山:四川大学, b.计算机学院, 成都 610065

联系人作者:联系作者

备注:李海彪(1989 —), 男, 河南新乡人, 硕士生, 研究方向为目标跟踪。

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引用该论文

LI Hai-biao,HUANG Shan. A Target Tracking Algorithm Based on Adaptive Kernelized Correlation Filtering[J]. Electronics Optics & Control, 2019, 26(4): 49-53

李海彪,黄 山. 基于自适应的核相关滤波的目标跟踪算法[J]. 电光与控制, 2019, 26(4): 49-53

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