  The paper focuses on a classical tracking model, subspace learning, grounded
on the fact that the targets in successive frames are considered to reside in a
low-dimensional subspace or manifold due to the similarity in their
appearances. In recent years, a number of subspace trackers have been proposed
and obtained impressive results. Inspired by the most recent results that the
tracking performance is boosted by the subspace with discrimination capability
learned over the recently localized targets and their immediately surrounding
background, this work aims at solving such a problem: how to learn a robust
low-dimensional subspace to accurately and discriminatively represent these
target and background samples. To this end, a discriminative approach, which
reliably separates the target from its surrounding background, is injected into
the subspace learning by means of joint learning, achieving a
dimension-adaptive subspace with superior discrimination capability. The
proposed approach is extensively evaluated and compared with the
state-of-the-art trackers on four popular tracking benchmarks. The experimental
results demonstrate that the proposed tracker performs competitively against
its counterparts. In particular, it achieves more than 9% performance increase
compared with the state-of-the-art subspace trackers.
