  Domain adaptation (DA) has demonstrated significant promise for real-time
nighttime unmanned aerial vehicle (UAV) tracking. However, the state-of-the-art
(SOTA) DA still lacks the potential object with accurate pixel-level location
and boundary to generate the high-quality target domain training sample. This
key issue constrains the transfer learning of the real-time daytime SOTA
trackers for challenging nighttime UAV tracking. Recently, the notable Segment
Anything Model (SAM) has achieved a remarkable zero-shot generalization ability
to discover abundant potential objects due to its huge data-driven training
approach. To solve the aforementioned issue, this work proposes a novel
SAM-powered DA framework for real-time nighttime UAV tracking, i.e., SAM-DA.
Specifically, an innovative SAM-powered target domain training sample swelling
is designed to determine enormous high-quality target domain training samples
from every single raw nighttime image. This novel one-to-many generation
significantly expands the high-quality target domain training sample for DA.
Comprehensive experiments on extensive nighttime UAV videos prove the
robustness and domain adaptability of SAM-DA for nighttime UAV tracking.
Especially, compared to the SOTA DA, SAM-DA can achieve better performance with
fewer raw nighttime images, i.e., the fewer-better training. This economized
training approach facilitates the quick validation and deployment of algorithms
for UAVs. The code is available at https://github.com/vision4robotics/SAM-DA.
