Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/101770
Title: | Object tracking via online metric learning | Authors: | Cong, Yang Yuan, Junsong Tang, Yandong |
Keywords: | DRNTU::Engineering::Electrical and electronic engineering | Issue Date: | 2012 | Conference: | IEEE International Conference on Image Processing (19th : 2012 : Orlando, Florida, US) | 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 http://hdl.handle.net/10220/12979 |
DOI: | 10.1109/ICIP.2012.6466884 | Schools: | School of Electrical and Electronic Engineering | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Conference Papers |
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