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2015 | OriginalPaper | Chapter

Patch-Based Visual Tracking with Two-Stage Multiple Kernel Learning

Authors : Heng Fan, Jinhai Xiang

Published in: Image and Graphics

Publisher: Springer International Publishing

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Abstract

In this paper a novel patch-based tracking algorithm is proposed by using two-stage multiple kernel learning. In the first stage, each object patch is represented with multiple features. Unlike simple feature combination, we utilize multiple kernel learning (MKL) method to obtain the optimal combination of multiple features and kernels, which assigns different weight to the features according to their discriminative power. In the second stage, we apply MKL to making full use of multiple patches of the target. This method can automatically distribute different weight to the object patches according to their importance, which improves the discriminative power of object patches as a whole. Within the Bayesian framework, we achieve object tracking by constructing a classifier, and the candidate with the maximum likelihood is chosen to be the target. Experiments demonstrate that the proposed tracking approach performs favorably against several state-of-the-art methods.

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Metadata
Title
Patch-Based Visual Tracking with Two-Stage Multiple Kernel Learning
Authors
Heng Fan
Jinhai Xiang
Copyright Year
2015
Publisher
Springer International Publishing
Book
Image and Graphics

Print ISBN: 978-3-319-21968-4

Electronic ISBN: 978-3-319-21969-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-21969-1_3

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