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Object Tracking using Reformative Transductive Learning with Sample Variational Correspondence Host Publication: 22nd ACM International Conference on Multimedia Authors: T. Zhuo, P. Zhang, Y. Zhang, W. Huang and H. Sahli Publication Year: 2014
Abstract: Tracking-by-learning strategies have effectively resolved many challenging problems for visual tracking. When labeled samples are limited, the learning performance can be improved by exploiting unlabeled ones. Thus, a key issue for semisupervised learning is the label assignment of the unlabeled samples, which is the principal focus of transductive learning.
Unfortunately, the optimization schemes employed by the transductive learning is hard to be applied to online tracking because of its large amount of computation for sample labeling. In this paper, a reformative transductive learning was proposed with the variational correspondence between the learning samples, which are utilized to build an effective matching cost function for more efficient label assignment during the learning of representative separators.By using a weighted accumulative average to update coefficients via a fixed budget of support vectors, the proposed tracking has been demonstrated to outperform most of the state-of-art trackers by comprehensive experiments on various benchmark videos.
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