Image sequence Prediction using ANN and RBFNN

A novel approach to predict future image frame of an image sequence is being presented. First, a method to predict the future position of a moving object in an image sequence is discussed using artificial neural network (ANN). Second, optical flow concept is used for generating complete image frame by calculating velocity of each pixel on both axes. A separate ANN (both sigmoidal and radial basis function neural network) is modeled for each pixel's velocity and predicted velocity of each pixel is then mapped to its future values and image frames are generated. The quality evaluations of predicted images are measured by Canny edge detection-based image comparison metric (CIM) and mean structure similarity index measure (MSSIM). These proposed approaches are found to generate future images up to 10 images successfully.

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