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《Computer Science》 2015-S2
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Machine Vision-based Lightweight Driver Assistance System

XU Bang-zhen;TANG Yi-ping;CAI Guo-ning;College of Information Engineering,Zhejiang University of Technology;  
This paper proposed a machine vision-based lightweight driver assistance system.Firstly,the adjusted algorithm for extracting edge and lane line detection algorithm are used to calibrate inside and outside parameters of cameras offline.Secondly,a multi-window division method identifying the actual distance is used on two-dimensional image according to the results of calibration,and different window is divided into regions of different safety factor according to distance,in order to provide prior knowledge of geometry of vision detection to the road.Thirdly,when there is an obstacle in the area,the corresponding warning message is displayed to assist the driver and provide lightweight visual detection platform for intelligent driver assistance system.The proposed system in this paper can extract lane line on both sides of the vehicle quickly in car-board experiments and take advantage of off-line calibration results to generate alerts regions of different safety factors quickly,and both positive false detection rate and negative false detection rate in the experiment during normal driving in the lane are small and negligible.Compared with conventional driver assistance systems,our proposed method reduces the computation amount by simplifying the detection process to achieve lightweight lane and vehicle detection,and lays the foundation for implementation of the system on embedded systems.
【Fund】: 国家基金面上项目:主动三维立体全景视觉传感技术研究(61070134)资助
【CateGory Index】: TP391.41
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