  Correlation filters are special classifiers designed for shift-invariant
object recognition, which are robust to pattern distortions. The recent
literature shows that combining a set of sub-filters trained based on a single
or a small group of images obtains the best performance. The idea is equivalent
to estimating variable distribution based on the data sampling (bagging), which
can be interpreted as finding solutions (variable distribution approximation)
directly from sampled data space. However, this methodology fails to account
for the variations existed in the data. In this paper, we introduce an
intermediate step -- solution sampling -- after the data sampling step to form
a subspace, in which an optimal solution can be estimated. More specifically,
we propose a new method, named latent constrained correlation filters (LCCF),
by mapping the correlation filters to a given latent subspace, and develop a
new learning framework in the latent subspace that embeds distribution-related
constraints into the original problem. To solve the optimization problem, we
introduce a subspace based alternating direction method of multipliers (SADMM),
which is proven to converge at the saddle point. Our approach is successfully
applied to three different tasks, including eye localization, car detection and
object tracking. Extensive experiments demonstrate that LCCF outperforms the
state-of-the-art methods. The source code will be publicly available.
https://github.com/bczhangbczhang/.

Computer Vision and Pattern Recognition