Visual tracking via multi-view semi-supervised learning

In this paper, we present a novel visual object tracking model via multi-view semi-supervised learning. Instead of concatenating multiple views into a single view directly to adapt to conventional machine learning algorithms, the combination of views is learned by exploiting the consensus of distinct views in the entire tracking. Besides, semi-supervised learning alleviates the lack of sufficient labeled samples in the tracking task, resulting in significant improvement in generalization performance. By showing that the sample data is block-circulant, we diagonalize it with the Discrete Fourier Transform to keep the tracking at high speed. Using features extracted by the VGG-19 network and in a 1:1 ratio of the labeled samples to the unlabeled, the experiment results on the CVPR2013 Online Object Tracking Benchmark show the effectiveness of our multi-view semi-supervised tracking model.