Generation of future image frames using autoregressive model


This research work entails a novel approach to generate future image frames for a given sequence of images by tracking positions of pixels of selected past frames using optical flow. The corresponding pixels' tracks are viewed as a time series and are modeled using an Autoregressive Model. The resulting model is used to generate future tracks which in essence are the future positions of the respective pixels. To generate future images, the last known intensity values of respective pixels are mapped to their future position. The proposed approach is applied on a fighter plane image sequence and on a moving car sequence. In both cases, we have successfully generated 20 future image frames using a sequence of 50 image frames in the training set. Quality assessment is done using Canny edge detection based Image Comparison Metric (CIM) and Mean Structural Similarity Index Measure (MSSIM). The results and assessment values obtained are appreciable.


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