Date of Award
2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair
Tathagata Mukherjee
Committee Member
Jacob Hauenstein
Committee Member
Farbod Fahimi
Research Advisor
Tathagata Mukherjee
Subject(s)
Pattern recognition systems, Machine learning, Image processing--Digital techniques
Abstract
In this thesis, a framework for predicting future frames from videos of naturally occurring processes will be presented. This is achieved by learning the underlying physical laws of the natural process through the exploitation of spatiotemporal features from the frames of the video. Our framework defines windows containing frames at varying time intervals, which we call dilations, and uses them to predict future frames using a 3D convolutional neural network (CNN). The network architecture accepts six time-dilated windows of input video frames and computes spatiotemporal features for each window maintaining the exclusion of the resulting features across three phases of the network, finally aggregating the features at the end for computing the final predictions. We tested the proposed framework with two datasets; the first is a video of ocean waves and the second one is a video of clouds in the sky.
Recommended Citation
Robinson, Benjamin, "A mutually exclusive 3D convolution based time dilation framework for future frame prediction in video streams of natural events" (2025). Theses. 797.
https://louis.uah.edu/uah-theses/797