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Object Tracking via Online Metric Learning
Cong Y(丛杨); Yuan JS(袁浚菘); Tang YD(唐延东)
Conference Name2012 IEEE International Conference on Image Processing
Conference DateSeptember 30 - October 3, 2012
Conference PlaceOrland, Florida, USA
Author of SourceIEEE Signal Processing Society
Source PublicationProceedings of the 2012 IEEE International Conference on Image Processing
Publication PlaceNew York, USA
Indexed ByEI ; CPCI(ISTP)
EI Accession number20131116112322
WOS IDWOS:000319334900100
Contribution Rank1
KeywordTracking Metric Learning Semi-supervised Learning Online Learning
AbstractBy 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.
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Document Type会议论文
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, China
2.Nanyang Technological University, Singapore, Singapore
Recommended Citation
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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