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.
Highlights
Traffic accidents have become a major cause of death
Considering only the detected object regions of potential vehicles in single frames may lead to the following problems in the segmentation process and the spatial clustering process: (1) a preceding vehicle may be too close to another vehicle moving parallel or street lamps, so that they may be occluded during the segmentation process, and detected as one connected region; (2) an oncoming vehicle may be passing so close to the host car that it may be occluded by the reflected beams on the road, and be merged into one large connected region; and (3) the headlight set or taillight set of a vehicle may include multiple light pairs, and they may not be immediately merged into a single group by the spatial clustering process
This study presents a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system)
Summary
Traffic accidents have become a major cause of death. Most traffic accidents are caused by driver carelessness under traffic conditions. To provide a satisfactory solution for these issues, this study adopts a heterogamous dual-processor embedded system platform, and the traffic event video recording function and vision-based driver assistance modules, including vehicle detection, traffic condition analysis, and driver warning functions are implemented and optimized using the computational resources of the two heterogamous processors. The proposed VIDASS system includes the computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording functions by processing the road-scene frames in front of the host car, which are captured from the CCD sensors mounted inside the host vehicle These proposed vision-based sensing and processing technologies are implemented as a set of embedded software component and modules based on a component-based system framework, and are integrated and performed on an ARM-DSP heterogamous dual-core embedded platform. Experimental results show that the proposed system provides both efficiency and feasible advantages for integrated vehicle detection, collision warning, and traffic event recording for driver surveillance in various nighttime road environments and different traffic conditions
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