IEICE TRANSACTIONS on Information
A Neural-Based Surveillance System for Detecting Dangerous Non-frontal Gazes for Car Drivers
Summary :
This paper presents the design of an automatic surveillance system to monitor the dangerous non-frontal gazes of the car driver. To track the driver's eyes, we propose a novel filter to locate the "between-eye", which is the middle point between the two eyes, to help the fast locating of eyes. We also propose a specially designed criterion function named mean ratio function to accurately locate the positions of eyes. To analyze the gazes of the driver, a multilayer perceptron neural network is trained to examine whether the driver is losing the proper gaze or not. By incorporating the neural network output with some well-designed alarm-issuing rules, the system performs the monitoring task for single dedicated driver and multiple different drivers with a satisfied performance in our experiments.
- Publication
- IEICE TRANSACTIONS on Information Vol.E87-D No.9 pp.2229-2238
- Publication Date
- 2004/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
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Cheng-Chin CHIANG, Chi-Lun HUANG, "A Neural-Based Surveillance System for Detecting Dangerous Non-frontal Gazes for Car Drivers" in IEICE TRANSACTIONS on Information,
vol. E87-D, no. 9, pp. 2229-2238, September 2004, doi: .
Abstract: This paper presents the design of an automatic surveillance system to monitor the dangerous non-frontal gazes of the car driver. To track the driver's eyes, we propose a novel filter to locate the "between-eye", which is the middle point between the two eyes, to help the fast locating of eyes. We also propose a specially designed criterion function named mean ratio function to accurately locate the positions of eyes. To analyze the gazes of the driver, a multilayer perceptron neural network is trained to examine whether the driver is losing the proper gaze or not. By incorporating the neural network output with some well-designed alarm-issuing rules, the system performs the monitoring task for single dedicated driver and multiple different drivers with a satisfied performance in our experiments.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_9_2229/_p
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@ARTICLE{e87-d_9_2229,
author={Cheng-Chin CHIANG, Chi-Lun HUANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Neural-Based Surveillance System for Detecting Dangerous Non-frontal Gazes for Car Drivers},
year={2004},
volume={E87-D},
number={9},
pages={2229-2238},
abstract={This paper presents the design of an automatic surveillance system to monitor the dangerous non-frontal gazes of the car driver. To track the driver's eyes, we propose a novel filter to locate the "between-eye", which is the middle point between the two eyes, to help the fast locating of eyes. We also propose a specially designed criterion function named mean ratio function to accurately locate the positions of eyes. To analyze the gazes of the driver, a multilayer perceptron neural network is trained to examine whether the driver is losing the proper gaze or not. By incorporating the neural network output with some well-designed alarm-issuing rules, the system performs the monitoring task for single dedicated driver and multiple different drivers with a satisfied performance in our experiments.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - A Neural-Based Surveillance System for Detecting Dangerous Non-frontal Gazes for Car Drivers
T2 - IEICE TRANSACTIONS on Information
SP - 2229
EP - 2238
AU - Cheng-Chin CHIANG
AU - Chi-Lun HUANG
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2004
AB - This paper presents the design of an automatic surveillance system to monitor the dangerous non-frontal gazes of the car driver. To track the driver's eyes, we propose a novel filter to locate the "between-eye", which is the middle point between the two eyes, to help the fast locating of eyes. We also propose a specially designed criterion function named mean ratio function to accurately locate the positions of eyes. To analyze the gazes of the driver, a multilayer perceptron neural network is trained to examine whether the driver is losing the proper gaze or not. By incorporating the neural network output with some well-designed alarm-issuing rules, the system performs the monitoring task for single dedicated driver and multiple different drivers with a satisfied performance in our experiments.
ER -
English
