  High quality object proposals are crucial in visual tracking algorithms that
utilize region proposal network (RPN). Refinement of these proposals, typically
by box regression and classification in parallel, has been popularly adopted to
boost tracking performance. However, it still meets problems when dealing with
complex and dynamic background. Thus motivated, in this paper we introduce an
improved proposal refinement module, Cascaded Regression-Align-Classification
(CRAC), which yields new state-of-the-art performances on many benchmarks.
  First, having observed that the offsets from box regression can serve as
guidance for proposal feature refinement, we design CRAC as a cascade of box
regression, feature alignment and box classification. The key is to bridge box
regression and classification via an alignment step, which leads to more
accurate features for proposal classification with improved robustness. To
address the variation in object appearance, we introduce an
identification-discrimination component for box classification, which leverages
offline reliable fine-grained template and online rich background information
to distinguish the target from background. Moreover, we present pyramid
RoIAlign that benefits CRAC by exploiting both the local and global cues of
proposals. During inference, tracking proceeds by ranking all refined proposals
and selecting the best one. In experiments on seven benchmarks including
OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT
exhibits very promising results in comparison with state-of-the-art competitors
and runs in real-time.
