Feature extraction for classification of different weather conditions

Abstract

Classification of different weather conditions provides a first step support for outdoor scene modeling, which is a core component in many different applications of outdoor video analysis and computer vision. Features derived from intrinsic properties of the visual effects of different weather conditions contribute to successful classification. In this paper, features representing both the autocorrelation of pixel-wise intensities over time and the max directional length of rain streaks or snowflakes are proposed. Based on the autocorrelation of each pixel’s intensities over time, two temporal features are used for coarse classification of weather conditions according to their visual effects. On the other hand, features are extracted for fine classification of video clips with rain and snow. The classification results on 249 video clips associated with different weather conditions indicate the effectiveness of the extracted features, by using C-SVM as the classifier.