Fitting Parametric Road Models to Spatio-Temporal Derivatives
Abstract
We present an algorithm for extracting parametric descriptions of roads from motion cues inherent in static video of traffic. First, statistics of spatio-temporal derivatives are accumulated from a static video. We derive new energy terms for fitting B-spline snakes to roads by aligning their direction and speed to be maximally consistent with the spatio-temporal derivatives. Due to the constraint on the derivative of the snake, we are able to produce stable, open-ended snakes, without specifying the location of endpoints. We explore other energy terms, including terms to expand the width of the snake allowing it to cover the entire road.
Cite
Text
Georg and Pless. "Fitting Parametric Road Models to Spatio-Temporal Derivatives." IEEE/CVF International Conference on Computer Vision Workshops, 2009. doi:10.1109/ICCVW.2009.5457671Markdown
[Georg and Pless. "Fitting Parametric Road Models to Spatio-Temporal Derivatives." IEEE/CVF International Conference on Computer Vision Workshops, 2009.](https://mlanthology.org/iccvw/2009/georg2009iccvw-fitting/) doi:10.1109/ICCVW.2009.5457671BibTeX
@inproceedings{georg2009iccvw-fitting,
title = {{Fitting Parametric Road Models to Spatio-Temporal Derivatives}},
author = {Georg, Manfred and Pless, Robert},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2009},
pages = {421-427},
doi = {10.1109/ICCVW.2009.5457671},
url = {https://mlanthology.org/iccvw/2009/georg2009iccvw-fitting/}
}