@article{ref1,
title="Stereo-Based Pedestrian Detection for Collision-Avoidance Applications",
journal="IEEE transactions on intelligent transportation systems",
year="2009",
author="Nedevschi, S. and Bota, S. and Tomiuc, C.",
volume="10",
number="3",
pages="380-391",
abstract="Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them. We present a new approach for standing- and walking-pedestrian detection, in urban traffic conditions, using grayscale stereo cameras mounted on board a vehicle. Our system uses pattern matching and motion for pedestrian detection. Both 2-D image intensity information and 3-D dense stereo information are used for classification. The 3-D data are used for effective pedestrian hypothesis generation, scale and depth estimation, and 2-D model selection. The scaled models are matched against the selected hypothesis using high-performance matching, based on the Chamfer distance. Kalman filtering is used to track detected pedestrians. A subsequent validation, based on the motion field's variance and periodicity of tracked walking pedestrians, is used to eliminate false positives.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2008.2012373",
url="http://dx.doi.org/10.1109/TITS.2008.2012373"
}