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题名:
Object Tracking via Online Metric Learning
作者: 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
通讯作者: 丛杨
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: Proceedings of the 2012 IEEE International Conference on Image Processing
会议录出版者: IEEE
会议录出版地: New York, USA
出版日期: 2012
页码: 417-420
收录类别: EI ; CPCI(ISTP)
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
产权排序: 1
EI收录号: 20131116112322
WOS记录号: WOS:000319334900100
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/10212
Appears in Collections:机器人学研究室_会议论文

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Recommended Citation:
Cong Y,Yuan JS,Tang YD. Object Tracking via Online Metric Learning[C]. 2012 IEEE International Conference on Image Processing. Orland, Florida, USA. September 30 - October 3, 2012.Object Tracking via Online Metric Learning.
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