To monitor their networks, road operators equip them with cameras. Degraded meteorological conditions alter the transportation system operation by modifying the behavior of drivers and by reducing the operation range of the sensors. A vision-based traffic monitoring system is proposed to take fog and rain into account and react accordingly. A background modeling approach, based on a mixture of gaussians, is used to separate the foreground from the background. Since fog is steady weather, the background image is used to detect and quantify it and to restore the images. Since rain is a dynamic phenomenon, the foreground is used to detect it and rain streaks are removed from the images accordingly. The different detection algorithms are described and illustrated using actual images to show their potential benefits. The algorithms may be implemented in existing video-based traffic monitoring systems and allow the multiplication of applications running on roadside cameras.