Detection And Removal Of Snow From Videos

Current video systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from"bad"weather. Outdoor video systems are used in various applications, such as surveillance, autonomous navigation, remote sensing license plate recognition and military field, but none of these systems account for the effects of weather conditions. Hence, research of the impacts of snow on images is of great military sense and significant social and economic benefits.Weather conditions are commonly studied in atmosphere science area. In communication science area, researchers have studied the effects on wave transmission. The weather effects on video haven't been studied widely. Most of the studies only consider the steady atmosphere conditions, like fog, mist and haze. These studies recover the weather-polluted images through certain optical models. However, the effects of snow on a camera viewing a scene in a natural environment are very different and remain unexplored. In order to remove the effects of snow from videos we need to study the physical properties of snow. After that we can model the effects of snow on images and develop algorithms to remove them. We begin by studying the effects of bad weather conditions. Weather conditions can be broadly classified as steady (fog, mist and haze) and dynamic (rain, snow and hail). Their effects on images are analyzed. After these analyses, we use a correlation model that captures the dynamics of snow, a physics-based photometry model that describe the snow intensity in image. Based on these models, we develop an efficient algorithm (DRS) for detecting and removing snow from videos. The estimation of photometric model parameters is also given. The effectiveness of the models and algorithm is demonstrated through experiments.