Pedestrian detection using wavelet templates
Oren, M.
Papageorgiou, C.
Sinha, P.
Osuna, E.
Poggio, T.
Artificial Intelligence Lab., MIT, Cambridge, MA ;
This paper appears in: Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
Publication Date: 17-19 Jun 1997
On page(s): 193-199
Meeting Date: 06/17/1997 - 06/19/1997
Location: San Juan, Puerto Rico
ISBN: 0-8186-7822-4
References Cited: 18
INSPEC Accession Number: 5637700
Digital Object Identifier: 10.1109/CVPR.1997.609319
Current Version Published: 2002-08-06
Abstract
This paper presents a trainable object detection architecture that
is applied to detecting people in static images of cluttered scenes.
This problem poses several challenges. People are highly non-rigid
objects with a high degree of variability in size, shape, color, and
texture. Unlike previous approaches, this system learns from examples
and does not rely on any a priori (hand-crafted) models or on motion.
The detection technique is based on the novel idea of the wavelet
template that defines the shape of an object in terms of a subset of the
wavelet coefficients of the image. It is invariant to changes in color
and texture and can be used to robustly define a rich and complex class
of objects such as people. We show how the invariant properties and
computational efficiency of the wavelet template make it an effective
tool for object detection
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