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Object tracking via online metric learning | IEEE Conference Publication | IEEE Xplore

Object tracking via online metric learning


Abstract:

By considering visual tracking as a similarity matching problem, we propose a self-supervised tracking method that incorporates adaptive metric learning and semi-supervis...Show More

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.
Date of Conference: 30 September 2012 - 03 October 2012
Date Added to IEEE Xplore: 21 February 2013
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Conference Location: Orlando, FL, USA

References

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