中国科学院沈阳自动化研究所机构知识库
Advanced  
SIA OpenIR  > 机器人学研究室  > 期刊论文
题名:
Speeded up Low Rank Online Metric Learning for Object Tracking
作者: Cong Y(丛杨); Fan BJ(范保杰); Liu J(刘霁); Luo JB(罗杰波); Yu HB(于海斌)
作者部门: 机器人学研究室
通讯作者: Cong Y(丛杨)
关键词: online learning ; metric learning ; low rank,object tracking ; semi-supervised learning
刊名: IEEE Transactions on Circuits and Systems for Video Technology
ISSN号: 1051-8215
出版日期: 2015
卷号: 25, 期号:6, 页码:922-934
收录类别: SCI ; EI
EI收录号: 20155201733939
WOS记录号: WOS:000357616000003
产权排序: 1
摘要: Visual object tracking can be considered as an online procedure to adaptively measure the foreground object similarity itself. However, many previous works usually adopt a fixed metric or offline metric learning to evaluate this dynamic process; even with some online metric learning trackers, their models often suffer from overfitting issues. To overcome these deficiencies, we propose a self-supervised tracking method that incorporates adaptive metric learning and semi-supervised learning into a unified framework. For similarity measurement, we design a new online metric learning model via low rank constraint to handle overfitting. Specially, we employ the max norm instead of the trace norm used in our previous work. This not only maintains the low rank property to overcome overfitting, but also reduces the computational complexity from O(n3) to O(n2), such that the new model is more suitable for object tracking. Moreover, by associating the information from stored training templates with unlabeled testing samples, a bi-linear graph is defined accordingly to propagate the label of each sample. High-confidence samples are then collected for self-training the model and updating the templates concurrently to handle large-scale. Experiments on various benchmark datasets and comparisons to several stateof- the-art methods demonstrate the effectiveness and efficiency of our algorithm. 
语种: 英语
WOS标题词: Science & Technology ; Technology
类目[WOS]: Engineering, Electrical & Electronic
关键词[WOS]: ROBUST VISUAL TRACKING ; MODEL ; SIMILARITY
研究领域[WOS]: Engineering
Citation statistics:
内容类型: 期刊论文
URI标识: http://210.72.131.170/handle/173321/15472
Appears in Collections:机器人学研究室_期刊论文

Files in This Item: Download All
File Name/ File Size Content Type Version Access License
Speeded up Low Rank Online Metric Learning for Object Tracking.pdf(6296KB)期刊论文出版稿开放获取View Download

作者单位: 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Department of Computer Science, University of Rochester, Rochester, NY, United States
3.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China
4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

Recommended Citation:
Cong Y,Fan BJ,Liu J,et al. Speeded up Low Rank Online Metric Learning for Object Tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology,2015,25(6):922-934.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Cong Y(丛杨)]'s Articles
[Fan BJ(范保杰)]'s Articles
[Liu J(刘霁)]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Cong Y(丛杨)]‘s Articles
[Fan BJ(范保杰)]‘s Articles
[Liu J(刘霁)]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
文件名: Speeded up Low Rank Online Metric Learning for Object Tracking.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2018  中国科学院沈阳自动化研究所 - Feedback
Powered by CSpace