A time, space and color-based classification of different weather conditions

Evaluation of different weather conditions provides a first step support for many different applications of outdoor video analysis and computer vision. In this paper, a simple but effective classification method on visual effects of different weather conditions is proposed. Due to the complex manifestations of weather conditions, we firstly provide a two-stage classification scheme. Then, we extract spatio-temporal and chromatic features to represent different weather situations. Using these features, we develop a classifier based on an experiential decision binary tree associated with C-SVM. The experimental results of classification on our newly-built video dataset indicate the effectiveness of our method.