| Authors: |
Genya Ogawa
and Katsuyuki Kise
and Tsuyoshi Torii
and Tomoharu Nagao
|
| Tags: |
algorithms,
analysis
automobiles,
board
camera
detection,
driving
genetic
human
image
map
on
pedestrian
processing,
programming,
recognition
recognition,
risk
system,
vehicle
|
| Abstract: |
To achieve a system that improves the safety and
comfort of the vehicle driving, a recognition system
equivalent to the human recognition ability should be
developed. However, the vehicle environment is
complicated and involves situations so diverse that a
uniform recognition processing approach cannot function
sufficiently. For a solution to this problem, we have
been studying a comprehensive risk recognition system,
which we call the risk map system, with learning
capability. As part of this paper, a system has been
developed that autonomously obtains the image
recognition processing. This paper presents a system as
an example that automatically learns through genetic
programming to obtain the image processing of
pedestrians and vehicles taken by an onboard camera
system |
@article{Ogawa:2007:TIE,
title = {Onboard Evolutionary Risk Recognition System for
Automobiles Toward the Risk Map System},
author = {Genya Ogawa and Katsuyuki Kise and Tsuyoshi Torii and Tomoharu Nagao},
journal = {IEEE Transactions on Industrial Electronics},
month = {April},
number = {2},
pages = {878--886},
volume = {54},
year = {2007},
abstract = {To achieve a system that improves the safety and
comfort of the vehicle driving, a recognition system
equivalent to the human recognition ability should be
developed. However, the vehicle environment is
complicated and involves situations so diverse that a
uniform recognition processing approach cannot function
sufficiently. For a solution to this problem, we have
been studying a comprehensive risk recognition system,
which we call the risk map system, with learning
capability. As part of this paper, a system has been
developed that autonomously obtains the image
recognition processing. This paper presents a system as
an example that automatically learns through genetic
programming to obtain the image processing of
pedestrians and vehicles taken by an onboard camera
system},
issn = {0278-0046}, size = {9 pages}, notes = {picking out pedestrian silhouettes from 256 gray level
CCD image sequences (4 frames). ACTIT. bloat size
penalty. 500 generations. Fuji Heavy Industries Ltd.}, doi = {10.1109/TIE.2007.891654},
keywords = {algorithms, analysis automobiles, board camera detection, driving genetic human image map on pedestrian processing, programming, recognition recognition, risk system, vehicle }
}
::