  Transformer-based visual object tracking has been utilized extensively.
However, the Transformer structure is lack of enough inductive bias. In
addition, only focusing on encoding the global feature does harm to modeling
local details, which restricts the capability of tracking in aerial robots.
Specifically, with local-modeling to global-search mechanism, the proposed
tracker replaces the global encoder by a novel local-recognition encoder. In
the employed encoder, a local-recognition attention and a local element
correction network are carefully designed for reducing the global redundant
information interference and increasing local inductive bias. Meanwhile, the
latter can model local object details precisely under aerial view through
detail-inquiry net. The proposed method achieves competitive accuracy and
robustness in several authoritative aerial benchmarks with 316 sequences in
total. The proposed tracker's practicability and efficiency have been validated
by the real-world tests.
