中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Twofold correlation filtering for tracking integration

文献类型:期刊论文

作者Wang, Wei1,2; Li, Weiguang1,2; Chen, Zhaoming1; Shi, Mingquan1
刊名IEICE Transactions on Information and Systems
出版日期2018
卷号E101D期号:10页码:2547-2550
ISSN号09168532
DOI10.1587/transinf.2018EDL8100
英文摘要In general, effective integrating the advantages of different trackers can achieve unified performance promotion. In this work, we study the integration of multiple correlation filter (CF) trackers; propose a novel but simple tracking integration method that combines different trackers in filter level. Due to the variety of their correlation filter and features, there is no comparability between different CF tracking results for tracking integration. To tackle this, we propose twofold CF to unify these various response maps so that the results of different tracking algorithms can be compared, so as to boost the tracking performance like ensemble learning. Experiment of two CF methods integration on the data sets OTB demonstrates that the proposed method is effective and promising. © 2018 The Institute of Electronics, Information and Communication Engineers.
电子版国际标准刊号17451361
语种英语
源URL[http://119.78.100.138/handle/2HOD01W0/8058]  
专题智能工业设计工程中心
作者单位1.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China;
2.University of Chinese Academy of Sciences, China
推荐引用方式
GB/T 7714
Wang, Wei,Li, Weiguang,Chen, Zhaoming,et al. Twofold correlation filtering for tracking integration[J]. IEICE Transactions on Information and Systems,2018,E101D(10):2547-2550.
APA Wang, Wei,Li, Weiguang,Chen, Zhaoming,&Shi, Mingquan.(2018).Twofold correlation filtering for tracking integration.IEICE Transactions on Information and Systems,E101D(10),2547-2550.
MLA Wang, Wei,et al."Twofold correlation filtering for tracking integration".IEICE Transactions on Information and Systems E101D.10(2018):2547-2550.

入库方式: OAI收割

来源:重庆绿色智能技术研究院

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