  Due to the lack of large-scale labeled Thermal InfraRed (TIR) training
datasets, most existing TIR trackers are trained directly on RGB datasets.
However, tracking methods trained on RGB datasets suffer a significant drop-off
in TIR data due to the domain shift issue. To this end, in this work, we
propose a Progressive Domain Adaptation framework for TIR Tracking (PDAT),
which transfers useful knowledge learned from RGB tracking to TIR tracking. The
framework makes full use of large-scale labeled RGB datasets without requiring
time-consuming and labor-intensive labeling of large-scale TIR data.
Specifically, we first propose an adversarial-based global domain adaptation
module to reduce domain gap on the feature level coarsely. Second, we design a
clustering-based subdomain adaptation method to further align the feature
distributions of the RGB and TIR datasets finely. These two domain adaptation
modules gradually eliminate the discrepancy between the two domains, and thus
learn domain-invariant fine-grained features through progressive training.
Additionally, we collect a largescale TIR dataset with over 1.48 million
unlabeled TIR images for training the proposed domain adaptation framework.
Experimental results on five TIR tracking benchmarks show that the proposed
method gains a nearly 6% success rate, demonstrating its effectiveness.
