  Discriminative correlation filters (DCF) and siamese networks have achieved
promising performance on visual tracking tasks thanks to their superior
computational efficiency and reliable similarity metric learning, respectively.
However, how to effectively take advantages of powerful deep networks, while
maintaining the real-time response of DCF, remains a challenging problem.
Embedding the cross-correlation operator as a separate layer into siamese
networks is a popular choice to enhance the tracking accuracy. Being a key
component of such a network, the correlation layer is updated online together
with other parts of the network. Yet, when facing serious disturbance, fused
trackers may still drift away from the target completely due to accumulated
errors. To address these issues, we propose a coarse-to-fine tracking
framework, which roughly infers the target state via an online-updating DCF
module first and subsequently, finely locates the target through an
offline-training asymmetric siamese network (ASN). Benefitting from the
guidance of DCF and the learned channel weights obtained through exploiting the
given ground-truth template, ASN refines feature representation and implements
precise target localization. Systematic experiments on five popular tracking
datasets demonstrate that the proposed DCF-ASN achieves the state-of-the-art
performance while exhibiting good tracking efficiency.
