A Prior-knowledge based Casted Shadows Prediction Model Featuring OpenStreetMap Data

M. Rogez, L. Tougne, L. Robinault

Abstract

We present a prior-knowledge based shadow prediction model, focused on outdoors scene, which allows to predict pixels, on the camera, which are likely to be part of shadows casted by surrounded buildings. We employ a geometrical approach which models surrounding buildings, their shadow and the camera. One innovative aspect of our method is to retrieve building datas automatically from OpenStreetMap, a community project providing free geographic data. We provide both qualitative and quantitative results in two different contexts to assess performance of our prediction model. While our method cannot achieve pixel precision easily alone, it opens opportunities for more elaborate shadow detection algorithms and occlusion-aware models.

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Paper Citation


in Harvard Style

Rogez M., Tougne L. and Robinault L. (2013). A Prior-knowledge based Casted Shadows Prediction Model Featuring OpenStreetMap Data . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 602-607. DOI: 10.5220/0004212206020607


in Bibtex Style

@conference{visapp13,
author={M. Rogez and L. Tougne and L. Robinault},
title={A Prior-knowledge based Casted Shadows Prediction Model Featuring OpenStreetMap Data},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={602-607},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004212206020607},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - A Prior-knowledge based Casted Shadows Prediction Model Featuring OpenStreetMap Data
SN - 978-989-8565-47-1
AU - Rogez M.
AU - Tougne L.
AU - Robinault L.
PY - 2013
SP - 602
EP - 607
DO - 10.5220/0004212206020607