Trainable pedestrian detection
Papageorgiou, C.
Poggio, T.
Artificial Intelligence Lab., MIT, Cambridge, MA ;
This paper appears in: Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on
Publication Date: 1999
Volume: 4,
On page(s): 35-39 vol.4
Meeting Date: 10/24/1999 - 10/28/1999
Location: Kobe, Japan
ISBN: 0-7803-5467-2
References Cited: 18
INSPEC Accession Number: 6522036
Digital Object Identifier: 10.1109/ICIP.1999.819462
Current Version Published: 2002-08-06
Abstract
Robust, fast object detection systems are critical to the success
of next-generation automotive vision systems. An important criteria is
that the detection system be easily configurable to a new domain or
environment. In this paper, we present work on a general object
detection system that can be trained to detect different types of
objects; we focus on the task of pedestrian detection. This paradigm of
learning from examples allows us to avoid the need for a hand-crafted
solution. Unlike many pedestrian detection systems, the core technique
does not rely on motion information and makes no assumptions on the
scene structured or the number of objects present. We discuss an
extension to the system that takes advantage of dynamical information
when processing video sequences to enhance accuracy. We also describe a
real, real-time version of the system that has been integrated into a
DaimlerChrysler test vehicle
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