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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.


System block diagram.
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f1-sensors-12-02373: System block diagram.

Mentions: The proposed vision-based intelligent nighttime driver assistance and surveillance (VIDASS) system integrates effective vision-based sensing and processing modules, including nighttime vehicle detection, collision warning determination, and event recording functionalities. These functions are implemented to identify target vehicles in front of the host car, estimating their distances, determine possible collision accidents, and record traffic event videos. The real-time vision-based sensing and processing modules of the proposed VIDASS system include bright object segmentation, spatial clustering process, rule-based vehicle identification, vehicle distance estimation, and traffic event warning and control signaling machinery. The following subsections describe these features. Figure 1 shows a flow diagram of the proposed vision processing modules for the VIDASS system.


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)

System block diagram.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-12-02373: System block diagram.
Mentions: The proposed vision-based intelligent nighttime driver assistance and surveillance (VIDASS) system integrates effective vision-based sensing and processing modules, including nighttime vehicle detection, collision warning determination, and event recording functionalities. These functions are implemented to identify target vehicles in front of the host car, estimating their distances, determine possible collision accidents, and record traffic event videos. The real-time vision-based sensing and processing modules of the proposed VIDASS system include bright object segmentation, spatial clustering process, rule-based vehicle identification, vehicle distance estimation, and traffic event warning and control signaling machinery. The following subsections describe these features. Figure 1 shows a flow diagram of the proposed vision processing modules for the VIDASS system.

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.