Generation of Future Image Frames for an Image Sequence
A way of generating the future frames of an image sequence is presented. In this paper first we present a way of predicting the future positions of rigid moving objects in a given sequence of images from a static camera.The moving object is first extracted from the images and its centroid is found as a measure of its position. These positions are used to find the future positions of the object using Artificial Neural Network models. This approach is found to predict the positions with very good accuracy. Next we give an algorithm for generating complete future image frames. The optical flow of the images is calculated to find the velocity of each pixel. Time series of the velocities are constructed for each pixel for both dimensions. A separate neural network model is used to predict the future velocities of each pixel and the pixels are then mapped to their new positions. Two different types of neural network models(sigmoidal function networks and radial basis function networks) have been used.
KeywordsNeural Network Image Sequence Test Image Neural Network Model Optical Flow
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