Towards Safer Transportation: a self-supervised learning approach for traffic video deraining
This addresses video quality issues for traffic management and enforcement during inclement weather, but appears incremental as it builds on existing deraining methods.
The study tackled the problem of rain streaks degrading traffic video quality by proposing a two-stage self-supervised learning method for deraining, resulting in satisfactory performance in visual quality and Peak Signal-Noise Ratio values.
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.