Snowfall detection under bad visibility scene
Hiroshi Kawarabuki, Kazunori Onoguchi · 2014
An effect of weather is very critical in outdoor camera surveillance applications. So, we focus on bad weather by snow and we propose an algorithm of detecting snowfall from surveillance camera images. The proposed algorithm increases a contrast of an image by haze removal. A contrast of hazy image is reduced by haze color of atmosphere. When several haze colors exist in an image, the degree of contrast degradation is different for each haze color. To deal with this problem, the proposed method performs segmentation of an image for every haze color and estimates the airlight, the transmission, and the weight of dehaze in each segmentation area individually. Most of haze removal algorithms require a tone curve correction as post processing. However, it is very difficult to select an optimal tone curve correction for any image. To deal with this problem, we propose a novel algorithm that does not require any tone curve correction. The general haze removal algorithm treats only haze image. However, in the field of outdoor camera surveillance, it's desirable to realize the algorithm which can be applied to various weather conditions. Our algorithm can detect snowfall at high speed and stably not only in bad weather but also in good weather.