Abstract
Discriminative Correlation Filters (DCF) are efficient in
visual tracking but suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested
to resolve this issue by enforcing spatial penalty on DCF
coefficients, which, inevitably, improves the tracking performance at the price of increasing complexity. To tackle
online updating, SRDCF formulates its model on multiple
training images, further adding difficulties in improving ef-
ficiency. In this work, by introducing temporal regularization to SRDCF with single sample, we present our spatialtemporal regularized correlation filters (STRCF). The STRCF formulation can not only serve as a reasonable approximation to SRDCF with multiple training samples, but also
provide a more robust appearance model than SRDCF in
the case of large appearance variations. Besides, it can
be efficiently solved via the alternating direction method of
multipliers (ADMM). By incorporating both temporal and
spatial regularization, our STRCF can handle boundary effects without much loss in efficiency and achieve superior
performance over SRDCF in terms of accuracy and speed.
Compared with SRDCF, STRCF with hand-crafted features
provides a 5× speedup and achieves a gain of 5.4% and
3.6% AUC score on OTB-2015 and Temple-Color, respectively. Moreover, STRCF with deep features also performs
favorably against state-of-the-art trackers and achieves an
AUC score of 68.3% on OTB-2015