中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 视频内容安全团队
Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning

文献类型：期刊论文

作者Gao, Jin1; Wang, Qiang1; Xing, Junliang1; Ling, Haibin2; Hu, Weiming1; Maybank, Stephen3
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2020-04-01
卷号42期号:4页码:939-955
关键词Task analysis Correlation Target tracking Probability distribution Visualization Collaboration Visual tracking Gaussian processes correlation filters transfer learning tracking-by-fusion
ISSN号0162-8828
DOI10.1109/TPAMI.2018.2889070
通讯作者Hu, Weiming(wmhu@nlpr.ia.ac.cn)
英文摘要This paper presents a new Gaussian Processes (GPs)-based particle filter tracking framework. The framework non-trivially extends Gaussian process regression (GPR) to transfer learning, and, following the tracking-by-fusion strategy, integrates closely two tracking components, namely a GPs component and a CFs one. First, the GPs component analyzes and models the probability distribution of the object appearance by exploiting GPs. It categorizes the labeled samples into auxiliary and target ones, and explores unlabeled samples in transfer learning. The GPs component thus captures rich appearance information over object samples across time. On the other hand, to sample an initial particle set in regions of high likelihood through the direct simulation method in particle filtering, the powerful yet efficient correlation filters (CFs) are integrated, leading to the CFs component. In fact, the CFs component not only boosts the sampling quality, but also benefits from the GPs component, which provides re-weighted knowledge as latent variables for determining the impact of each correlation filter template from the auxiliary samples. In this way, the transfer learning based fusion enables effective interactions between the two components. Superior performance on four object tracking benchmarks (OTB-2015, Temple-Color, and VOT2015/2016), and in comparison with baselines and recent state-of-the-art trackers, has demonstrated clearly the effectiveness of the proposed framework.
WOS关键词VISUAL TRACKING ; OBJECT TRACKING ; NETWORKS
资助项目Natural Science Foundation of China[61602478] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61472421] ; Beijing Natural Science Foundation[L172051] ; NSFC-general technology collaborative Fund for basic research[U1636218] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC040] ; CAS External cooperation key project ; Research Project of ForwardX Robotics, Inc. ; US NSF[1350521] ; US NSF[1618398] ; US NSF[1814745]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000526541100011
出版者IEEE COMPUTER SOC
资助机构Natural Science Foundation of China ; Beijing Natural Science Foundation ; NSFC-general technology collaborative Fund for basic research ; Key Research Program of Frontier Sciences, CAS ; CAS External cooperation key project ; Research Project of ForwardX Robotics, Inc. ; US NSF
源URL[http://ir.ia.ac.cn/handle/173211/38907]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者Hu, Weiming
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
2.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA
3.Birkbeck Coll, Dept Comp Sci & Informat Syst, Malet St, London WC1E 7HX, England
推荐引用方式
GB/T 7714		 Gao, Jin,Wang, Qiang,Xing, Junliang,et al. Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2020,42(4):939-955.
APA Gao, Jin,Wang, Qiang,Xing, Junliang,Ling, Haibin,Hu, Weiming,&Maybank, Stephen.(2020).Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,42(4),939-955.
MLA Gao, Jin,et al."Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 42.4(2020):939-955.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。