  Discriminatively learned correlation filters (DCF) have been widely used in
online visual tracking filed due to its simplicity and efficiency. These
methods utilize a periodic assumption of the training samples to construct a
circulant data matrix, which implicitly increases the training samples and
reduces both storage and computational complexity.The periodic assumption also
introduces unwanted boundary effects. Recently, Spatially Regularized
Correlation Filters (SRDCF) solved this issue by introducing penalization on
correlation filter coefficients depending on their spatial location. However,
SRDCF's efficiency dramatically decreased due to the breaking of circulant
structure.
  We propose Faster Spatially Regularized Discriminative Correlation Filters
(FSRDCF) for tracking. The FSRDCF is constructed from Ridge Regression, the
circulant structure of training samples in the spatial domain is fully used,
more importantly, we further exploit the circulant structure of regularization
function in the Fourier domain, which allows our problem to be solved more
directly and efficiently. Experiments are conducted on three benchmark
datasets: OTB-2013, OTB-2015 and VOT2016. Our approach achieves equivalent
performance to the baseline tracker SRDCF on all three datasets. On OTB-2013
and OTB-2015 datasets, our approach obtains a more than twice faster running
speed and a more than third times shorter start-up time than the SRDCF. For
state-of-the-art comparison, our approach demonstrates superior performance
compared to other non-spatial-regularization trackers.
