IEICE TRANSACTIONS on Information
Robust Vehicle Detection under Poor Environmental Conditions for Rear and Side Surveillance
Summary :
During the period from dusk to dark, when it is difficult for drivers to see other vehicles, or when visibility is poor due to rain, snow, etc., the contrast between nearby vehicles and the background is lower. Under such conditions, conventional surveillance systems have difficulty detecting the outline of nearby vehicles and may thus fail to recognize them. To solve this problem, we have developed a rear and side surveillance system for vehicles that uses image processing. The system uses two stereo cameras to monitor the areas to the rear and sides of a vehicle, i.e., a driver's blind spots, and to detect the positions and relative speeds of other vehicles. The proposed system can estimate the shape of a vehicle from a partial outline of it, thus identifying the vehicle by filling in the missing parts of the vehicle outline. Testing of the system under various environmental conditions showed that the rate of errors (false and missed detection) in detecting approaching vehicles was reduced to less than 10%, even under conditions that are problematic for conventional processing.
- Publication
- IEICE TRANSACTIONS on Information Vol.E87-D No.1 pp.97-104
- Publication Date
- 2004/01/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision Applications)
- Category
- ITS
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Osafumi NAKAYAMA, Morito SHIOHARA, Shigeru SASAKI, Tomonobu TAKASHIMA, Daisuke UENO, "Robust Vehicle Detection under Poor Environmental Conditions for Rear and Side Surveillance" in IEICE TRANSACTIONS on Information,
vol. E87-D, no. 1, pp. 97-104, January 2004, doi: .
Abstract: During the period from dusk to dark, when it is difficult for drivers to see other vehicles, or when visibility is poor due to rain, snow, etc., the contrast between nearby vehicles and the background is lower. Under such conditions, conventional surveillance systems have difficulty detecting the outline of nearby vehicles and may thus fail to recognize them. To solve this problem, we have developed a rear and side surveillance system for vehicles that uses image processing. The system uses two stereo cameras to monitor the areas to the rear and sides of a vehicle, i.e., a driver's blind spots, and to detect the positions and relative speeds of other vehicles. The proposed system can estimate the shape of a vehicle from a partial outline of it, thus identifying the vehicle by filling in the missing parts of the vehicle outline. Testing of the system under various environmental conditions showed that the rate of errors (false and missed detection) in detecting approaching vehicles was reduced to less than 10%, even under conditions that are problematic for conventional processing.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_1_97/_p
Copy
@ARTICLE{e87-d_1_97,
author={Osafumi NAKAYAMA, Morito SHIOHARA, Shigeru SASAKI, Tomonobu TAKASHIMA, Daisuke UENO, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Vehicle Detection under Poor Environmental Conditions for Rear and Side Surveillance},
year={2004},
volume={E87-D},
number={1},
pages={97-104},
abstract={During the period from dusk to dark, when it is difficult for drivers to see other vehicles, or when visibility is poor due to rain, snow, etc., the contrast between nearby vehicles and the background is lower. Under such conditions, conventional surveillance systems have difficulty detecting the outline of nearby vehicles and may thus fail to recognize them. To solve this problem, we have developed a rear and side surveillance system for vehicles that uses image processing. The system uses two stereo cameras to monitor the areas to the rear and sides of a vehicle, i.e., a driver's blind spots, and to detect the positions and relative speeds of other vehicles. The proposed system can estimate the shape of a vehicle from a partial outline of it, thus identifying the vehicle by filling in the missing parts of the vehicle outline. Testing of the system under various environmental conditions showed that the rate of errors (false and missed detection) in detecting approaching vehicles was reduced to less than 10%, even under conditions that are problematic for conventional processing.},
keywords={},
doi={},
ISSN={},
month={January},}
Copy
TY - JOUR
TI - Robust Vehicle Detection under Poor Environmental Conditions for Rear and Side Surveillance
T2 - IEICE TRANSACTIONS on Information
SP - 97
EP - 104
AU - Osafumi NAKAYAMA
AU - Morito SHIOHARA
AU - Shigeru SASAKI
AU - Tomonobu TAKASHIMA
AU - Daisuke UENO
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2004
AB - During the period from dusk to dark, when it is difficult for drivers to see other vehicles, or when visibility is poor due to rain, snow, etc., the contrast between nearby vehicles and the background is lower. Under such conditions, conventional surveillance systems have difficulty detecting the outline of nearby vehicles and may thus fail to recognize them. To solve this problem, we have developed a rear and side surveillance system for vehicles that uses image processing. The system uses two stereo cameras to monitor the areas to the rear and sides of a vehicle, i.e., a driver's blind spots, and to detect the positions and relative speeds of other vehicles. The proposed system can estimate the shape of a vehicle from a partial outline of it, thus identifying the vehicle by filling in the missing parts of the vehicle outline. Testing of the system under various environmental conditions showed that the rate of errors (false and missed detection) in detecting approaching vehicles was reduced to less than 10%, even under conditions that are problematic for conventional processing.
ER -
English
