A probabilistic model for flood detection in video sequences
Paulo Vinicius Koerich Borges, Joceli Mayer, Ebroul Izquierdo · 2008
In this paper we propose a new image event detection method for identifying flood in videos. Traditional image based flood detection is often used in remote sensing and satellite imaging applications. In contrast, the proposed method is applied for retrieval of flood catastrophes in newscast content, which present great variation in flood and background characteristics, depending on the video instance. Different flood regions in different images share some common features which are reasonably invariant to lightness, camera angle or background scene. These features are texture, relation among color channels and saturation characteristics. The method analyses the frame-to-frame change in these features and the results are combined according to the Bayes classifier to achieve a decision (i.e. flood happens, flood does not happen). In addition, because the flooded region is usually located around the lower and middle parts of an image, a model for the probability of occurrence of flood as a function of the vertical position is proposed, significantly improving the classification performance. Experiments illustrated the applicability of the method and the improved performance in comparison to other techniques.