[1]林椹尠,郑兴宁,吴成茂.结合模糊特征检测的鲁棒核相关滤波跟踪法[J].智能系统学报,2021,16(2):323-329.[doi:10.11992/tis.201912010]
 LIN Zhenxian,ZHENG Xingning,WU Chengmao.Robust KCF tracking algorithm combined with fuzzy feature detection[J].CAAI Transactions on Intelligent Systems,2021,16(2):323-329.[doi:10.11992/tis.201912010]
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结合模糊特征检测的鲁棒核相关滤波跟踪法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年2期
页码:
323-329
栏目:
学术论文—智能系统
出版日期:
2021-03-05

文章信息/Info

Title:
Robust KCF tracking algorithm combined with fuzzy feature detection
作者:
林椹尠1 郑兴宁2 吴成茂3
1. 西安邮电大学 理学院,陕西 西安 710121;
2. 西安邮电大学 通信与信息工程学院,陕西 西安 710121;
3. 西安邮电大学 电子工程学院,陕西 西安 710121
Author(s):
LIN Zhenxian1 ZHENG Xingning2 WU Chengmao3
1. School of Science, Xi’an University of Post and Telecommunications, Xi’an 710121, China;
2. School of Communication and Information Engineering, Xi’an University of Post and Telecommunications, Xi’an 710121, China;
3. School of Electronic Engineering, Xi’an University of Post and Telecommunications, Xi’an 710121, China
关键词:
计算机视觉目标跟踪核相关滤波法尺度不变特征变换局部二值模式模糊特征检测器图像清晰度评价特征匹配
Keywords:
computer visionvisual trackingkernel correlation filter (KCF)scale invariant feature transform (SIFT)local binary pattern (LBP)fuzzy feature detectorimage definition evaluation functionfeature matching
分类号:
TP391.41
DOI:
10.11992/tis.201912010
摘要:
针对跟踪领域内由于图像模糊而导致跟踪失败的问题,提出一种结合模糊特征检测的鲁棒核相关滤波(kernelized correlation filter, KCF)跟踪法。首先,将尺度不变特征变换(scale invariant feature transform, SIFT)描述子与局部二值模式(local binary pattern, LBP)算法结合,提取模糊图像中的特征点,并采用圆形邻域描述该特征点,以降低特征向量的维度,综合构建出模糊特征检测器。其次,设置图像清晰度阈值,若当前图像清晰度低于阈值,则启动模糊特征检测器,通过特征向量间的匹配,得出跟踪目标的位置;否则,通过传统的核相关滤波法预测目标位置。最后,在公开数据集OTB-2013和OTB-2015中的测试结果表明:与其他实验算法相比,该算法可对模糊图像中的目标进行有效跟踪且精度较高。
Abstract:
To address the tracking failure problem caused by image blur in the tracking field, we propose a robust kernelized correlation filter (KCF) tracking algorithm combined with fuzzy feature detection. First, the scale invariant feature transform descriptor is combined with the local binary pattern algorithm to extract the feature points in the fuzzy image, and the circular neighborhood is used to describe the feature points and reduce the dimensions of the feature vector. Thus, the fuzzy feature detector is constructed. Next, the image definition threshold is set. If the definition of the current image is lower than the threshold, fuzzy feature detection is initiated to obtain the tracking target position by matching the feature vectors. Otherwise, the target position is predicted by the traditional KCF algorithm. The test results on the open data sets OTB-2013 and OTB-2015 show that the proposed algorithm can effectively track targets in fuzzy images with higher accuracy than other experimental algorithms

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备注/Memo

备注/Memo:
收稿日期:2019-12-09。
基金项目:国家自然科学基金项目(61671377);陕西省自然科学基金项目(2018JM4018)
作者简介:林椹尠,教授,博士,主要研究方向为小波理论及其在信号、图像处理中的应用。发表学术论文近30篇;郑兴宁,硕士研究生,主要研究方向为图像处理、目标跟踪;吴成茂,高级工程师,主要研究方向为模式分析与智能信息处理、图像处理与信息安全。发表学术论文200余篇
通讯作者:郑兴宁.E-mail:1393608820@qq.com
更新日期/Last Update: 2021-04-25