Object Tracking using Reformative Transductive Learning with Sample Variational Correspondence

Tracking-by-learning strategies have effectively solved 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 semi-supervised learning is the label assignment of the unlabeled samples, which is the principal focus of transductive learning. Unfortunately, the optimization scheme 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 the coefficients via a fixed budget of support vectors, the proposed tracking has been demonstrated to outperform most of the state-of-art trackers.