Limits...
A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture.

Chen YL, Chiang HH, Chiang CY, Liu CM, Yuan SM, Wang JH - Sensors (Basel) (2012)

Bottom Line: The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle.These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform.Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan. ylchen@csie.ntut.edu.tw

ABSTRACT
This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.

No MeSH data available.


Headlight control process work flow.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3376597&req=5

f7-sensors-12-02373: Headlight control process work flow.

Mentions: The proposed traffic event warning and recording subsystem is an automatic control process in the proposed system. This automatic control process includes a vehicle headlight control process and a traffic event video recording process. When any oncoming vehicles are detected, the headlight control process automatically switches the headlights to low beam, and then reverts to high beams once the detected vehicles leave the detection zone. The warning voice and traffic event video recording process are also activated to notify drivers to slowly decelerate when the distance from the detected preceding vehicles is too small, and to record a traffic event video using the MPEG4 video codec [27,28]. Figures 7 and 8 present flowcharts of the headlight control process and the traffic event video recording process, respectively.


A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture.

Chen YL, Chiang HH, Chiang CY, Liu CM, Yuan SM, Wang JH - Sensors (Basel) (2012)

Headlight control process work flow.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376597&req=5

f7-sensors-12-02373: Headlight control process work flow.
Mentions: The proposed traffic event warning and recording subsystem is an automatic control process in the proposed system. This automatic control process includes a vehicle headlight control process and a traffic event video recording process. When any oncoming vehicles are detected, the headlight control process automatically switches the headlights to low beam, and then reverts to high beams once the detected vehicles leave the detection zone. The warning voice and traffic event video recording process are also activated to notify drivers to slowly decelerate when the distance from the detected preceding vehicles is too small, and to record a traffic event video using the MPEG4 video codec [27,28]. Figures 7 and 8 present flowcharts of the headlight control process and the traffic event video recording process, respectively.

Bottom Line: The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle.These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform.Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan. ylchen@csie.ntut.edu.tw

ABSTRACT
This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.

No MeSH data available.