Stationarity and Correlation Test of Image Sequences Based Classification on Scenes with Different Weather Conditions

Classification on different weather conditions provides first step support for outdoor scene modeling, which is a core component in many different applications of image sequence analysis and computer vision. In this paper, an objective classification method on scenes with different weather conditions is presented with two steps based on stationarity and correlation test of image sequences. First of all, scenes with various weather conditions are considerably described, on which an objective classification standard is accentuated. Secondly, based on the stationarity test on sub-sequences of intensity averages with counter order, the corresponding expectation and deviation of patterns are formulated and proved. Therefore, scenes with different weather conditions are primarily classified into stationary and nonstationary ones. Finally, a correlation test on autocorrelation function of intensity values in image sequences with different weather conditions is organized. Moreover, descriptions on sudden change of the autocorrelation function are established. Consequently, a classification on static or dynamic scene is ultimately accomplished. The two-step method needs no parameters, which avoids estimating the parameters of population distribution, when the inference of classification standard is in progress. This method is demonstrated to be effective using experiments on seven videos with different weather conditions, which contributes to latter applications such as scene modeling with different weather conditions.