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|Title:||Object tracking via online metric learning||Authors:||Cong, Yang
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2012||Abstract:||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.||URI:||https://hdl.handle.net/10356/101770
|DOI:||10.1109/ICIP.2012.6466884||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
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