  Most existing Siamese-based tracking methods execute the classification and
regression of the target object based on the similarity maps. However, they
either employ a single map from the last convolutional layer which degrades the
localization accuracy in complex scenarios or separately use multiple maps for
decision making, introducing intractable computations for aerial mobile
platforms. Thus, in this work, we propose an efficient and effective
hierarchical feature transformer (HiFT) for aerial tracking. Hierarchical
similarity maps generated by multi-level convolutional layers are fed into the
feature transformer to achieve the interactive fusion of spatial (shallow
layers) and semantics cues (deep layers). Consequently, not only the global
contextual information can be raised, facilitating the target search, but also
our end-to-end architecture with the transformer can efficiently learn the
interdependencies among multi-level features, thereby discovering a
tracking-tailored feature space with strong discriminability. Comprehensive
evaluations on four aerial benchmarks have proven the effectiveness of HiFT.
Real-world tests on the aerial platform have strongly validated its
practicability with a real-time speed. Our code is available at
https://github.com/vision4robotics/HiFT.
