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
this https URL

本文提出了一种基于 Siamese 的跟踪方法，采用分层特征变换对多级卷积层生成的分层相似度图进行交互式融合，提高了全局的上下文信息，更高效地学习多级特征之间的相互依赖关系，并且实现了在实时速度情况下的实际应用，该方法在四个空中基准测试中被证明具有很好的效果。