@article{ref1,
title="Stereo- and neural network-based pedestrian detection",
journal="IEEE transactions on intelligent transportation systems",
year="2000",
author="Zhao, L. and Thorpe, C.E.",
volume="1",
number="3",
pages="148-154",
abstract="Pedestrian detection is essential to avoid dangerous traffic situations. We present a fast and robust algorithm for detecting pedestrians in a cluttered scene from a pair of moving cameras. This is achieved through stereo-based segmentation and neural network-based recognition. The algorithm includes three steps. First, we segment the image into sub-image object candidates using disparities discontinuity. Second, we merge and split the sub-image object candidates into sub-images that satisfy pedestrian size and shape constraints. Third, we use intensity gradients of the candidate sub-images as input to a trained neural network for pedestrian recognition. The experiments on a large number of urban street scenes demonstrate that the proposed algorithm: (1) can detect pedestrians in various poses, shapes, sizes, clothing, and occlusion status; (2) runs in real-time; and (3) is robust to illumination and background changes<p />",
language="",
issn="1524-9050",
doi="10.1109/6979.892151",
url="http://dx.doi.org/10.1109/6979.892151"
}