dc.contributor.authorCong, Yang
dc.contributor.authorYuan, Junsong
dc.contributor.authorTang, Yandong
dc.date.accessioned2013-08-05T03:21:07Z
dc.date.available2013-08-05T03:21:07Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10220/12979
dc.description.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.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleObject tracking via online metric learningen_US
dc.typeConference Paper
dc.contributor.conferenceIEEE International Conference on Image Processing (19th : 2012 : Orlando, Florida, US)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICIP.2012.6466884


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