Night-time vehicle detection using D-S evidence theory
Zhou Fang-li · Jisuanji yingyong yanjiu · 2012
In order to effectively detect vehicles moving at night,this paper presented a new vehicle detection method for night-time vehicle using D-S evidence theory.Firstly,to collect all kind of contours of bright region in image,it segmented the road scene image by using threshold value in YCrCb color space and extracted the contour of each bright region,in which false targets such as non-rear light were excluded by using contour four-neighborhood red level method.Secondly,it paired the rear lamps using taillights clustering algorithm to get vehicles candidate hypothesis.At last,it used the structured feature information of taillights such as the area ratio,cross-image correlation and length-width ratio of the rear light combo box to construct the basic belief distribution function,and then constructed the general trust value by fusing these feature information and using D-S evidence theory,ultimately a belief threshold value was set to verify the vehicle hypothesis.The proposed method could decrease the number of subjective threshold,and reduced efficiently the risk of inappropriate threshold definition because of lacking experience,and thus could improve the recognition rate.The experimental results show that the method can improve the detection accuracy and reduce the misjudgment.It also improves the robustness of the system.