Deep Learning Approaches to Predict Future Frames in Videos

Abstract

Across several fields of computer vision, deep learning methods are gaining importance. While there have been several research on the categorization of pictures and movies, prediction of the coming frames of an input sequence in pixel-space has received very little attention, despite the fact that many applications may benefit from such information. Examples incorporate generated content and robotic agents that must function in natural situations and are autonomous. In actuality, learning how to predict the future of a picture sequence necessitates that the system comprehend and effectively store the content and characteristics for a certain amount of time. Since labelled data video data is rare and difficult to get, it is considered as a viable path that might even aid supervised jobs. Consequently, this paper provides a summary of scientific advancements pertaining to future frame predicting and offers a repeated network model that employs new approaches from research in deep learning. The suggested architecture is founded on the recurrent process responsible with multilayer cells, which enables spatial-temporal data similarities to be maintained. Powered by perceptually driven decision variables and a contemporary recurrent working towards achieving, it outperforms previous methods for future frame creation in multiple video content genres. All of this may be accomplished with fewer training cycles and model parameters.