On-Road Car Detection System Using VD-GMM 2.0 


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


PDF Full-Text
  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.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  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.

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880