On-Road Car Detection System Using VD-GMM 2.0 


Vol. 40,  No. 11, pp. 2291-2297, Nov.  2015


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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.

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  Cite this article

[IEEE Style]

O. Lee, I. Won, S. Lee, J. Kwon, "On-Road Car Detection System Using VD-GMM 2.0," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 11, pp. 2291-2297, 2015. DOI: .

[ACM Style]

Okmin Lee, Insu Won, Sangmin Lee, and Jangwoo Kwon. 2015. On-Road Car Detection System Using VD-GMM 2.0. The Journal of Korean Institute of Communications and Information Sciences, 40, 11, (2015), 2291-2297. DOI: .

[KICS Style]

Okmin Lee, Insu Won, Sangmin Lee, Jangwoo Kwon, "On-Road Car Detection System Using VD-GMM 2.0," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 11, pp. 2291-2297, 11. 2015.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880