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Authors: Yang Hu and Larry S. Davis

Affiliation: University of Maryland, United States

Keyword(s): Vehicle Detection, Context, Conditional Random Fields, Shadow, Ground, Orientation.

Abstract: Detecting vehicles in satellite images has a wide range of applications. Existing approaches usually identify vehicles from their appearance. They typically generate many false positives due to the existence of a large number of structures that resemble vehicles in the images. In this paper, we explore the use of context information to improve vehicle detection performance. In particular, we use shadows and the ground appearance around vehicles as context clues to validate putative detections. A data driven approach is applied to learn typical patterns of vehicle shadows and the surrounding “road-like” areas. By observing that vehicles often appear in parallel groups in urban areas, we also use the orientations of nearby detections as another context clue. A conditional random field (CRF) is employed to systematically model and integrate these different contextual knowledge. We present results on two sets of images from Google Earth. The proposed method significantly improves the per formance of the base appearance based vehicle detector. It also outperforms another state-of-the-art context model. (More)

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Paper citation in several formats:
Hu, Y. and S. Davis, L. (2013). Vehicle Detection with Context. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP; ISBN 978-989-8565-47-1; ISSN 2184-4321, SciTePress, pages 715-722. DOI: 10.5220/0004302907150722

@conference{visapp13,
author={Yang Hu. and Larry {S. Davis}.},
title={Vehicle Detection with Context},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP},
year={2013},
pages={715-722},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004302907150722},
isbn={978-989-8565-47-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP
TI - Vehicle Detection with Context
SN - 978-989-8565-47-1
IS - 2184-4321
AU - Hu, Y.
AU - S. Davis, L.
PY - 2013
SP - 715
EP - 722
DO - 10.5220/0004302907150722
PB - SciTePress

- Science and Technology Publications, Lda.
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