Vision-Based Rain Gauge for Dynamic Scenes

Cheen-Hau Tan, Jie Chen, Yun Ni, Lap-Pui Chau, Ling Min Soh

Abstract: In this paper we develop a vision-based rain intensity measurement method for dynamic scenes. The method first measures the area density of rain by analyzing temporal changes in pixel values in the video input. The area density, represented as a binary rain map, is then mapped to a rain intensity value using linear regression. To ensure temporal consistency of scene content across frames in dynamic scenes, we applied superpixel-based content alignment. Potential false detections in the binary rain map are removed using directional morphological opening. Experiments show that both superpixel-based content alignment and morphological opening are important for good rain map generation and rain intensity estimation.