  Robust and accurate visual tracking is one of the most challenging computer
vision problems. Due to the inherent lack of training data, a robust approach
for constructing a target appearance model is crucial. Recently,
discriminatively learned correlation filters (DCF) have been successfully
applied to address this problem for tracking. These methods utilize a periodic
assumption of the training samples to efficiently learn a classifier on all
patches in the target neighborhood. However, the periodic assumption also
introduces unwanted boundary effects, which severely degrade the quality of the
tracking model.
  We propose Spatially Regularized Discriminative Correlation Filters (SRDCF)
for tracking. A spatial regularization component is introduced in the learning
to penalize correlation filter coefficients depending on their spatial
location. Our SRDCF formulation allows the correlation filters to be learned on
a significantly larger set of negative training samples, without corrupting the
positive samples. We further propose an optimization strategy, based on the
iterative Gauss-Seidel method, for efficient online learning of our SRDCF.
Experiments are performed on four benchmark datasets: OTB-2013, ALOV++,
OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all
four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and
8.2% respectively, in mean overlap precision, compared to the best existing
trackers.
