Adaptive background estimation of underwater using Kalman-Filtering

Fast and accurate estimation of background model in video sequences is a basic task in many computer vision and video analysis applications. Underwater vision is a new area and the background of underwater has special quality such as unstable light spot, water ripple. To this end, this paper proposes an algorithm based on Kalman Filter, which is applied to the estimation of dynamic underwater background with a static monitoring camera of swimming pool´s bottom. Experimental on several underwater video sequences performing the model can efficiently adapt to the environmental of underwater.