When deploying pre-trained video object detectors in real-world scenarios,
the domain gap between training and testing data caused by adverse image
conditions often leads to performance degradation. Addressing this issue
becomes particularly challenging when only the pre-trained model and degraded
videos are available. Although various source-free domain adaptation (SFDA)
methods have been proposed for single-frame object detectors, SFDA for video
object detection (VOD) remains unexplored. Moreover, most unsupervised domain
adaptation works for object detection rely on two-stage detectors, while SFDA
for one-stage detectors, which are more vulnerable to fine-tuning, is not well
addressed in the literature. In this paper, we propose Spatial-Temporal
Alternate Refinement with Mean Teacher (STAR-MT), a simple yet effective SFDA
method for VOD. Specifically, we aim to improve the performance of the
one-stage VOD method, YOLOV, under adverse image conditions, including noise,
air turbulence, and haze. Extensive experiments on the ImageNetVOD dataset and
its degraded versions demonstrate that our method consistently improves video
object detection performance in challenging imaging conditions, showcasing its
potential for real-world applications.

我们提出了一种用于视频目标检测的简单而有效的无源域自适应方法，STAR-MT。通过改进 YOLOV 方法，在包括噪声、气流湍流和雾霾等恶劣图像条件下，我们的方法始终提高了视频目标检测性能，展示了其在现实世界应用中的潜力。