Stereo- and neural network-based pedestrian detection
Zhao, L.
Thorpe, C.E.
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA;
This paper appears in: Intelligent Transportation Systems, IEEE Transactions on
Publication Date: Sep 2000
Volume: 01,
Issue: 3
On page(s): 148-154
ISSN: 1524-9050
References Cited: 31
CODEN: ITISFG
INSPEC Accession Number: 6808489
Digital Object Identifier: 10.1109/6979.892151
Current Version Published: 2002-08-06
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
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