  In recent years, Convolutional Neural Network (CNN) based trackers have
achieved state-of-the-art performance on multiple benchmark datasets. Most of
these trackers train a binary classifier to distinguish the target from its
background. However, they suffer from two limitations. Firstly, these trackers
cannot effectively handle significant appearance variations due to the limited
number of positive samples. Secondly, there exists a significant imbalance of
gradient contributions between easy and hard samples, where the easy samples
usually dominate the computation of gradient. In this paper, we propose a
robust tracking method via Statistical Positive sample generation and Gradient
Aware learning (SPGA) to address the above two limitations. To enrich the
diversity of positive samples, we present an effective and efficient
statistical positive sample generation algorithm to generate positive samples
in the feature space. Furthermore, to handle the issue of imbalance between
easy and hard samples, we propose a gradient sensitive loss to harmonize the
gradient contributions between easy and hard samples. Extensive experiments on
three challenging benchmark datasets including OTB50, OTB100 and VOT2016
demonstrate that the proposed SPGA performs favorably against several
state-of-the-art trackers.
