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We combine these features and use an online boosting approach to create the specific person classifier.
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Grabner et al.[1] have proposed a tracker via online boosting.
The tracking task is formulated as a binary classification via online boosting framework.
The remainder of this article is organized as follows: Section 2 gives a short review of online boosting algorithm.
A major challenge in the online boosting algorithms is how to choose the positive and negative samples.
Inspired by the visual saliency detection approach, we propose a visual saliency detection-based sample selection unifying with online boosting approach for robust object tracking.
The basic idea of online boosting is that the importance λ of a sample can be estimated by propagating it through a fixed set of weak classifiers.
In online boosting, since no distribution on the samples is available, a distribution on the weak classifiers is maintained in the work by Oza and Russell[20].
In[1], online boosting is not directly performed on the weak classifiers, but on the "selectors".
The weak classifiers are updated with the obtained samples in the online boosting framework.
Grabner et al.[1] introduced online boosting algorithm to the object tracking and demonstrated successful tracking of objects on various sequences.
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