[1]宁欣,李卫军,田伟娟,等.一种自适应模板更新的判别式KCF跟踪方法[J].智能系统学报,2019,14(01):121-126.[doi:10.11992/tis.201806038]
 NING Xin,LI Weijun,TIAN Weijuan,et al.Adaptive template update of discriminant KCF for visual tracking[J].CAAI Transactions on Intelligent Systems,2019,14(01):121-126.[doi:10.11992/tis.201806038]
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一种自适应模板更新的判别式KCF跟踪方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年01期
页码:
121-126
栏目:
出版日期:
2019-01-05

文章信息/Info

Title:
Adaptive template update of discriminant KCF for visual tracking
作者:
宁欣12 李卫军123 田伟娟2 徐驰2 徐健1
1. 中国科学院半导体研究所 高速电路与神经网络实验室, 北京 100083;
2. 威富集团 形象认知计算联合实验室, 北京 100083;
3. 中国科学院大学 微电子学院, 北京 100029
Author(s):
NING Xin12 LI Weijun123 TIAN Weijuan2 XU Chi2 XU Jian1
1. Laboratory of Artificial Neural Networks and High-speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;
2. Image Cognitive Computing Joint Lab, Wave Group, Beijing 100083, China;
3. School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100029, China
关键词:
目标跟踪目标检测高速核相关滤波算法模板更新卷积神经网络
Keywords:
visual trackingobject detectionhigh-speed kernelized correlation filterstemplate updateconvolution neural network
分类号:
TP391
DOI:
10.11992/tis.201806038
摘要:
为了解决单目标跟踪算法中存在的目标旋转、遮挡和快速运动等挑战,提出了一种基于自适应更新策略的判别式核相关滤波器(kernelized correlation filter,KCF)目标跟踪新框架。构建了外观判别式模型,实现跟踪质量有效性的评估。构造了新的自适应模板更新策略,能够有效区分目标跟踪异常时当前目标是否发生了旋转。提出了一种结合目标检测的跟踪新构架,能够进一步有效判别快速运动和遮挡状态。同时,针对上述3种挑战,分别采用模板更新、目标运动位移最小化以及目标检测算法实现目标跟踪框的恢复,保证了跟踪的有效性和长期性。实验分别采用2种传统手动特征HOG和CN(color names)验证提出的框架鲁棒性,结果证明了提出的目标跟踪新方法在速度和精度方面的优越性能。
Abstract:
To solve the challenges of in-plane/out-of-plane rotation (IPR/OPR), fast motion (FM), and occlusion (OCC), a new robust visual tracking framework of discriminant kernelized correlation filter (KCF) based on adaptive template update strategy is presented in this paper. Specifically, the proposed discriminant models were first used to determine the tracking validity and then a new adaptive template update strategy was introduced to effectively distinguish whether or not the object has rotated when the object tracking was abnormal. Furthermore, a new visual tracking framework combining object test is presented, which could further effectively distinguish FM and OCC. Meanwhile, to overcome the above-mentioned challenges, three measures were taken to recover the object tracking frame:template updating, object movement displacement minimization, and use of an object detection algorithm ensuring validity and long-term visual tracking. We implemented two versions of the proposed tracker with representations from two conventional hand-actuated features, histogram of oriented gradient (HOG), and color names (CN) to validate the strong compatibility of the algorithm. Experimental results demonstrated the state-of-the-art performance in tracking accuracy and speed for processing the cases of IPR/OPR, FM, and OCC.

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备注/Memo

备注/Memo:
收稿日期:2018-06-22。
基金项目:国家自然科学基金项目(61572458).
作者简介:宁欣,男,1989年生,博士,IEEE、中国计算机学会、中国人工智能学会、中国自动化学会会员,主要研究方向为计算机视觉、类脑视觉计算、形象认知计算和神经网络。发表学术论文15篇,被SCI收录5篇;李卫军,男,1975年生,教授,博士生导师,博士,主要研究方向为机器视觉、模式识别与智能系统、仿生图像处理技术、仿生模式识别理论与方法、近红外光谱定性分析技术、高维信息计算。主持国家自然科学基金项目2项,企业合作研究项目3项。发表学术论文30篇;田伟娟,女,1990年生,硕士研究生,主要研究方向为视频、图像处理和目标跟踪相关算法。
通讯作者:李卫军.E-mail:wjli@semi.ac.cn
更新日期/Last Update: 1900-01-01