Removal of dynamic weather conditions based on variable time window

Dynamic weather conditions, which mainly include rain and snow, make prevailing algorithms for many applications of outdoor video analysis and computer vision lapse. To remove dynamic weather conditions, the authors propose a pixel-wise framework combining a detection method with a removal approach. Dynamic weather conditions are detected by a strategy-driven state transition, which integrates static initialisation using K-means clustering with dynamic maintenance of Gaussian mixture model. Moreover, a variable time window is presented for removal of rain and snow. Each component of the framework is addressed using detailed descriptions of corresponding algorithms. Experiments demonstrate the effectiveness of the method on detection and removal of dynamic weather conditions.