  Trackers based on Siamese network have shown tremendous success, because of
their balance between accuracy and speed. Nevertheless, with tracking scenarios
becoming more and more sophisticated, most existing Siamese-based approaches
ignore the addressing of the problem that distinguishes the tracking target
from hard negative samples in the tracking phase. The features learned by these
networks lack of discrimination, which significantly weakens the robustness of
Siamese-based trackers and leads to suboptimal performance. To address this
issue, we propose a simple yet efficient hard negative samples emphasis method,
which constrains Siamese network to learn features that are aware of hard
negative samples and enhance the discrimination of embedding features. Through
a distance constraint, we force to shorten the distance between exemplar vector
and positive vectors, meanwhile, enlarge the distance between exemplar vector
and hard negative vectors. Furthermore, we explore a novel anchor-free tracking
framework in a per-pixel prediction fashion, which can significantly reduce the
number of hyper-parameters and simplify the tracking process by taking full
advantage of the representation of convolutional neural network. Extensive
experiments on six standard benchmark datasets demonstrate that the proposed
method can perform favorable results against state-of-the-art approaches.
