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ECCV
2010
Springer

Discriminative Tracking by Metric Learning

9 years 2 months ago
Discriminative Tracking by Metric Learning
We present a discriminative model that casts appearance modeling and visual matching into a single objective for visual tracking. Most previous discriminative models for visual tracking are formulated as supervised learning of binary classifiers. The continuous output of the classification function is then utilized as the cost function for visual tracking. This may be less desirable since the function is optimized for making binary decision. Such a learning objective may make it not to be able to well capture the manifold structure of the discriminative appearances. In contrast, our unified formulation is based on a principled metric learning framework, which seeks for a discriminative embedding for appearance modeling. In our formulation, both appearance modeling and visual matching are performed online by efficient gradient based optimization. Our formulation is also able to deal with multiple targets, where the exclusive principle is naturally reinforced to handle occlusions. Its ef...
Added 09 Aug 2010
Updated 09 Aug 2010
Type Conference
Year 2010
Where ECCV
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