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Object Tracking via Online Metric Learning
Cong Y(丛杨); Yuan JS(袁浚菘); Tang YD(唐延东)
作者部门机器人学研究室
会议名称2012 IEEE International Conference on Image Processing
会议日期September 30 - October 3, 2012
会议地点Orland, Florida, USA
会议主办者IEEE Signal Processing Society
会议录名称Proceedings of the 2012 IEEE International Conference on Image Processing
出版者IEEE
出版地New York, USA
2012
页码417-420
收录类别EI ; CPCI(ISTP)
EI收录号20131116112322
WOS记录号WOS:000319334900100
产权排序1
ISBN号978-1-4673-2533-2
关键词Tracking Metric Learning Semi-supervised Learning Online Learning
摘要By considering visual tracking as a similarity matching problem, we propose a self-supervised tracking method that incorporates adaptive metric learning and semi-supervised learning into the framework of object tracking. For object representation, the spatial-pyramid structure is applied by fusing both the shape and texture cues as descriptors. A metric learner is adaptively trained online to best distinguish the foreground object and background, and a new bi-linear graph is defined accordingly to propagate the label of each sample. Then high-confident samples are collected to self-update the model to handle large-scale issue. Experiments on the benchmark dataset and comparisons with the state-of-the-art methods validate the advantages of our algorithm.
语种英语
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被引频次:5[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/10212
专题机器人学研究室
通讯作者Cong Y(丛杨)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, China
2.Nanyang Technological University, Singapore, Singapore
推荐引用方式
GB/T 7714
Cong Y,Yuan JS,Tang YD. Object Tracking via Online Metric Learning[C]//IEEE Signal Processing Society. New York, USA:IEEE,2012:417-420.
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