Video monitoring of traffic is useful for traffic management and control, traffic counting, and traffic law enforcement. However, traffic monitoring during inclement weather such as rain is a challenging task because video quality is corrupted by streaks of falling rain on the video image, and this hinders reliable characterization not only of the road environment but also of road-user behavior during such adverse weather events. This study proposes a two-stage self-supervised learning method to remove rain streaks in traffic videos. The first and second stages address intra- and inter-frame noise, respectively. The results indicated that the model exhibits satisfactory performance in terms of the image visual quality and the Peak Signal-Noise Ratio value.
@article{arxiv.2110.07379,
title = {Towards Safer Transportation: a self-supervised learning approach for traffic video deraining},
author = {Shuya Zong and Sikai Chen and Samuel Labi},
journal= {arXiv preprint arXiv:2110.07379},
year = {2021}
}
Comments
Under review for presentation at TRB 2022 Annual Meeting