  Deformable parts models show a great potential in tracking by principally
addressing non-rigid object deformations and self occlusions, but according to
recent benchmarks, they often lag behind the holistic approaches. The reason is
that potentially large number of degrees of freedom have to be estimated for
object localization and simplifications of the constellation topology are often
assumed to make the inference tractable. We present a new formulation of the
constellation model with correlation filters that treats the geometric and
visual constraints within a single convex cost function and derive a highly
efficient optimization for MAP inference of a fully-connected constellation. We
propose a tracker that models the object at two levels of detail. The coarse
level corresponds a root correlation filter and a novel color model for
approximate object localization, while the mid-level representation is composed
of the new deformable constellation of correlation filters that refine the
object location. The resulting tracker is rigorously analyzed on a highly
challenging OTB, VOT2014 and VOT2015 benchmarks, exhibits a state-of-the-art
performance and runs in real-time.

Computer Vision and Pattern Recognition