Nonlinear metric learning for visual tracking
We propose a nonlinear metric learning (NML) method for visual tracking. Instead of utilizing the hand-crafted similarity measures, the NML tracker can automatically learn distance metrics from training data itself to categorize object and backgrounds in visual tracking. To exploit the nonlinear structures of samples, the NML tracker seeks several hierarchical nonlinear transformations by adopting the neural network architectures to map candidates and template into a latent subspace where the distance of each positive pair is smaller than that of each negative pair. In this learned metric space, the candidate that maintains the minimum distance to the template is treated as the final tracking result. Evaluation on 20 challenging videos shows the efficacy of the NML tracker.