  The fully-convolutional siamese network based on template matching has shown
great potentials in visual tracking. During testing, the template is fixed with
the initial target feature and the performance totally relies on the general
matching ability of the siamese network. However, this manner cannot capture
the temporal variations of targets or background clutter. In this work, we
propose a novel gradient-guided network to exploit the discriminative
information in gradients and update the template in the siamese network through
feed-forward and backward operations. Our algorithm performs feed-forward and
backward operations to exploit the discriminative informaiton in gradients and
capture the core attention of the target. To be specific, the algorithm can
utilize the information from the gradient to update the template in the current
frame. In addition, a template generalization training method is proposed to
better use gradient information and avoid overfitting. To our knowledge, this
work is the first attempt to exploit the information in the gradient for
template update in siamese-based trackers. Extensive experiments on recent
benchmarks demonstrate that our method achieves better performance than other
state-of-the-art trackers.
