  Previous advances in object tracking mostly reported on favorable
illumination circumstances while neglecting performance at nighttime, which
significantly impeded the development of related aerial robot applications.
This work instead develops a novel unsupervised domain adaptation framework for
nighttime aerial tracking (named UDAT). Specifically, a unique object discovery
approach is provided to generate training patches from raw nighttime tracking
videos. To tackle the domain discrepancy, we employ a Transformer-based
bridging layer post to the feature extractor to align image features from both
domains. With a Transformer day/night feature discriminator, the daytime
tracking model is adversarially trained to track at night. Moreover, we
construct a pioneering benchmark namely NAT2021 for unsupervised domain
adaptive nighttime tracking, which comprises a test set of 180 manually
annotated tracking sequences and a train set of over 276k unlabelled nighttime
tracking frames. Exhaustive experiments demonstrate the robustness and domain
adaptability of the proposed framework in nighttime aerial tracking. The code
and benchmark are available at https://github.com/vision4robotics/UDAT.
