Rethinking Unsupervised Domain Adaptation for Nighttime Tracking

Despite the considerable progress that has been achieved in visual object tracking, it remains a challenge to track in low-light circumstances. Prior nighttime tracking methods suffer from either weak collaboration of cascade structures or the lack of pseudo supervision, and thus fail to bring out satisfactory results. In this paper, we develop a novel unsupervised domain adaptation framework for nighttime tracking. Specifically, we benefit from the establishment of pseudo supervision in the mean teacher network, and further extend it with three components at the input level and the optimization level. For the unlabeled target domain dataset, we first present an assignment-based object discovery strategy to generate suitable training patches. Additionally, a low-light enhancer is embedded to improve the pseudo labels that facilitate the following consistency learning. Finally, with the aid of better training data and pseudo labels, we replace the common mean square error with two stricter losses, which are entropy-decreasing classification consistency loss and confidence-weighted regression consistency loss, for better convergence. Experiments demonstrate that our proposed method achieves significant performance gains on multiple nighttime tracking benchmarks, and even brings slight enhancement on the source domain.