Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models
It addresses video frame prediction for applications like weather forecasting and autonomous systems, but is incremental as it combines existing methods.
This paper tackled video frame prediction by evaluating hybrid deep learning models combining autoencoders with temporal sequence methods like RNNs and 3D CNNs, resulting in SSIM metrics improving from 0.69 to 0.82, with 3D CNNs and ConvLSTMs being most effective.
In recent years, advances in Artificial Intelligence have significantly impacted computer science, particularly in the field of computer vision, enabling solutions to complex problems such as video frame prediction. Video frame prediction has critical applications in weather forecasting or autonomous systems and can provide technical improvements, such as video compression and streaming. Among Artificial Intelligence methods, Deep Learning has emerged as highly effective for solving vision-related tasks, although current frame prediction models still have room for enhancement. This paper evaluates several hybrid deep learning approaches that combine the feature extraction capabilities of autoencoders with temporal sequence modelling using Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (3D CNNs), and related architectures. The proposed solutions were rigorously evaluated on three datasets that differ in terms of synthetic versus real-world scenarios and grayscale versus color imagery. Results demonstrate that the approaches perform well, with SSIM metrics increasing from 0.69 to 0.82, indicating that hybrid models utilizing 3DCNNs and ConvLSTMs are the most effective, and greyscale videos with real data are the easiest to predict.