Long-term object tracking based on kernelized correlation filters

De-dong YANG; Yu-zhu* CAI; Ning MAO; Fu-cai YANG

doi:10.3788/OPE.20162408.2037

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Long-term object tracking based on kernelized correlation filters

Information Sciences | 更新时间：2020-07-07

- Long-term object tracking based on kernelized correlation filters
- Optics and Precision Engineering Vol. 24, Issue 8, Pages: 2037-2049(2016)
- 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：
- 基金信息：
- DOI：10.3788/OPE.20162408.2037
  CLC： TP391
- Received：18 April 2016，
  
  Accepted：22 June 2016，
  
  Published：25 August 2016
- 稿件说明：
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De-dong YANG, Yu-zhu* CAI, Ning MAO, et al. Long-term object tracking based on kernelized correlation filters[J]. Optics and precision engineering, 2016, 24(8): 2037-2049.
DOI：

De-dong YANG, Yu-zhu* CAI, Ning MAO, et al. Long-term object tracking based on kernelized correlation filters[J]. Optics and precision engineering, 2016, 24(8): 2037-2049. DOI： 10.3788/OPE.20162408.2037.

摘要

针对核相关滤波器(KCF)跟踪算法在目标跟踪中存在尺度变化、严重遮挡、相似目标干扰和出视野情况下跟踪失败等问题，提出了一种基于KCF的长期目标跟踪算法。该算法在分类器学习中加入空间正则化，利用基于样本区域空间位置信息的空间权重函数调节分类器系数，使分类器学习到更多负样本和未破坏的正样本，从而增强学习模型的判别力。然后，在检测区域利用Newton方法完成迭代处理，求取分类器最大响应位置及其目标尺度信息。最后，对最大响应位置的目标进行置信度比较，训练在线支持向量机(SVM)分类器，以便在跟踪失败的情况下，重新检测到目标而实现长期跟踪。采用OTB-2013评估基准50组视频序列验证了本文算法的有效性，并与30种其他跟踪方法进行了对比。结果表明：本文提出的算法跟踪精度为0.813，成功率为0.629，排名第一，相比传统KCF算法分别提高了9.86%和22.3%。在目标发生显著尺度变化、严重遮挡、相似目标干扰和出视野等复杂情况下，本文方法均具有较强的鲁棒性。

Abstract

As Kernelized Correlation Filters (KCF) tracking algorithm has poor performance in handling scale-variant

heavy occlusion

similar target interfere and out of view

this paper proposes a long-term tracking approach based on the KCF. Firstly

a spatial regularization component was introduced in the learning of a classifier

the classifier coefficients were penalized depending on the weight function of spatial location information in sample locations. By which the classifier could learn significantly larger set of negative training samples and uncorrupted positive samples

so that the discriminative power of learned model was increased. Then

the Newton method was used to complete the iteration and obtain the maximizing response location and target score of the classifier in the detection area. Finally

to re-detect the target in the case of tracking failure and achieve a long-term tracking

the confidence of the target with the maximum response score was compared and an online Support Vector Machine (SVM) classifier was trained. To verify the feasibility of the proposed algorithm

fifty groups of OTB-2013 benchmark video sequences were tested and the obtained results were compared with thirty kinds of other tracking algorithms. Experimental results indicate that the precision and success rate from the proposed method are respectively 0.813 and 0.629

ranking first. Compared with traditional KCF tracking algorithm

the proposed approach respectively improves by 9.86% and 22.3% in the precision and the success rate. Moreover

it is robust to significant scale changing

heavy occlusion

interfere with similar target

out of view and other complex scenes.

关键词

Keywords

references

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Related Institution

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

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