Object-centered Fourier Motion Estimation and Segment-Transformation Prediction
Abstract
e ability to anticipate the future is essential for action planning in autonomous systems. To this end, learning video pre-diction methods have been developed, but current systems often pro-duce blurred predictions. We address this issue by introducing an object-centered movement estimation, frame prediction, and correction frame-work using frequency-domain approaches. We transform single objects based on estimated translation and rotation speeds which we correct us-ing a learned encoding of the past. This results in clear predictions with few parameters. Experimental evaluation shows that our approach is accurate and efficient.
Keywords: Fourier, Prediction
Source code available at: https://github.com/v0lta/Fourier-Motion-Estimation-and-Segment-Transformation
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Bibtex
@INPROCEEDINGS{wolter2020object,
author = {Wolter, Moritz and Yao, Angela and Behnke, Sven},
title = {Object-centered Fourier Motion Estimation and Segment-Transformation Prediction},
booktitle = {28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine
Learning},
year = {2020},
keywords = {Fourier, Prediction},
abstract = {e ability to anticipate the future is essential for action planning in autonomous systems. To this
end, learning video pre-diction methods have been developed, but current systems often pro-duce
blurred predictions. We address this issue by introducing an object-centered movement estimation,
frame prediction, and correction frame-work using frequency-domain approaches. We transform single
objects based on estimated translation and rotation speeds which we correct us-ing a learned
encoding of the past. This results in clear predictions with few parameters. Experimental evaluation
shows that our approach is accurate and efficient.},
url = {https://vi.informatik.uni-bonn.de/papers/ESANN_2020_Wolter.pdf}
}