Measurements of rainfall rates from videos

Abstract

Measuring rainfall rates from videos is a novel research topic. Due to rain motion, reflection of light and background clutter, it is extremely challenging to obtain accurate measurements. In this paper, we propose a new technique for measuring rainfall rates from videos, which consists of the following technical steps: first, we detect raindrops in an image using gray-tone functions and direction of rain streaks; we then select the focused raindrops, based on two features: average color tensor response and average intensity difference. Afterwards, the size of the raindrops is estimated and a raindrop size distribution (RSD) curve is created according to the use of the RSD in meteorology. Finally, a rainfall rate is obtained by fitting the RSD curve with a Gamma distribution model. In the experiment section presented in this paper, the proposed algorithm is evaluated under different light, moderate and heavy rainy conditions. The measurement results of the proposed algorithm are consistent with those of a can-type rain gauge.