Large displacement optical flow based image predictor model

Abstract

This paper proposes a Large Displacement Optical Flow based Image Predictor Model for generating future image frames by applying past and present image frames. The predictor model is an Artificial Neural Network (ANN) and Radial Basis Function Neural Network (RBFNN) Model whose input set of data is horizontal and vertical components of velocities estimated using Large Displacement Optical Flow for every pixel intensity in a given image sequence. There has been a significant amount of research in the past to generate future image frames for a given set of image frames. The quality of generated images is evaluated by Canny's edge detection Index Metric (CIM) and Mean Structure Similarity Index Metric (MSSIM). For our proposed algorithm, CIM and MSSIM indices for all the future generated images are found better when compared with the most recent existing algorithms for future image frame generation. The objective of this study is to develop a generalized framework that can predict future image frames for any given image sequence with large displacements of objects. In this paper, we have validated our developed Image Predictor Model on an image sequence of landing jet fighter and obtained performance indices are found better as compared to most recent existing image predictor models.