Vehicle Detection with Context

Yang Hu, Larry S. Davis

2013

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 performance of the base appearance based vehicle detector. It also outperforms another state-of-the-art context model.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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 - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={715-722},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004302907150722},
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 - Vehicle Detection with Context
SN - 978-989-8565-47-1
AU - Hu Y.
AU - S. Davis L.
PY - 2013
SP - 715
EP - 722
DO - 10.5220/0004302907150722