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On-Road Car Detection System Using VD-GMM 2.0
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 Title & Authors
On-Road Car Detection System Using VD-GMM 2.0
Lee, Okmin; Won, Insu; Lee, Sangmin; Kwon, Jangwoo;
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 Abstract
This paper presents a vehicle detection system using the video as a input image what has moving of vehicles.. Input image has constraints. it has to get fixed view and downward view obliquely from top of the road. Road detection is required to use only the road area in the input image. In introduction, we suggest the experiment result and the critical point of motion history image extraction method, SIFT(Scale_Invariant Feature Transform) algorithm and histogram analysis to detect vehicles. To solve these problem, we propose using applied Gaussian Mixture Model(GMM) that is the Vehicle Detection GMM(VDGMM). In addition, we optimize VDGMM to detect vehicles more and named VDGMM 2.0. In result of experiment, each precision, recall and F1 rate is 9%, 53%, 15% for GMM without road detection and 85%, 77%, 80% for VDGMM2.0 with road detection.
 Keywords
Computer Vision;Digital Image Processing;Road Detection;Car Detection;GMM;
 Language
Korean
 Cited by
1.
우천 상황에 강인한 CLAHE를 적용한 Adaboost 기반 차량 검출 방법,강석준;한동석;

한국통신학회논문지, 2016. vol.41. 12, pp.1978-1984 crossref(new window)
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