Speeded up Low Rank Online Metric Learning for Object Tracking
文献类型：期刊论文
| 作者 | Cong Y(丛杨) ; Fan BJ(范保杰); Liu J(刘霁); Luo JB(罗杰波); Yu HB(于海波); Yu HB(于海斌)
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| 刊名 | IEEE Transactions on Circuits and Systems for Video Technology
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| 出版日期 | 2015 |
| 卷号 | 25期号:6页码:922-934 |
| 关键词 | online learning metric learning low rank,object tracking semi-supervised learning |
| ISSN号 | 1051-8215 |
| 产权排序 | 1 |
| 通讯作者 | 丛杨 |
| 中文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Engineering, Electrical & Electronic |
| 研究领域[WOS] | Engineering |
| 关键词[WOS] | ROBUST VISUAL TRACKING ; MODEL ; SIMILARITY |
| 收录类别 | SCI ; EI |
| 资助信息 | NSFC (61105013,61375014,61203270); the foundation of Chinese Scholarship Council. |
| 语种 | 英语 |
| WOS记录号 | WOS:000357616000003 |
| 公开日期 | 2014-12-29 |
| 源URL | [http://ir.sia.cn/handle/173321/15472]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 GB/T 7714 | 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. |
| APA | Cong Y,Fan BJ,Liu J,Luo JB,Yu HB,&Yu HB.(2015).Speeded up Low Rank Online Metric Learning for Object Tracking.IEEE Transactions on Circuits and Systems for Video Technology,25(6),922-934. |
| MLA | Cong Y,et al."Speeded up Low Rank Online Metric Learning for Object Tracking".IEEE Transactions on Circuits and Systems for Video Technology 25.6(2015):922-934. |
入库方式： OAI收割
来源：沈阳自动化研究所
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