Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Unmanned Enforcement System for Illegal Parking and Stopping Vehicle using Adaptive Gaussian Mixture Model

적응적 가우시안 혼합 모델을 이용한 불법주정차 무인단속시스템

  • Youm, Sungkwan (Dept. of Information & Communication Engineering, Wonkwang University) ;
  • Shin, Seong-Yoon (Dept. of Computer Information Engineering, Kunsan National University) ;
  • Shin, Kwang-Seong (Dept. of Digital Contents Engineering, Wonkwang University) ;
  • Pak, Sang-Hyon (Management Support Team, DICS Vision)
  • Received : 2021.03.11
  • Accepted : 2021.03.15
  • Published : 2021.03.31
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Abstract

As the world is trying to establish smart city, unmanned vehicle control systems are being widely used. This paper writes about an unmanned parking control system that uses an adaptive background image modeling method, suggesting the method of updating the background image, modeled with an adaptive Gaussian mixture model, in both global and local way according to the moving object. Specifically, this paper focuses on suggesting two methods; a method of minimizing the influence of a moving object on a background image and a method of accurately updating the background image by quickly removing afterimages of moving objects within the area of interest to be monitored. In this paper, through the implementation of the unmanned vehicle control system, we proved that the proposed system can quickly and accurately distinguish both moving and static objects such as vehicles from the background image.

최근 스마트 도시를 구축하기 위해 무인 차량 관제 시스템의 보급이 활성화 되고 있다. 본 논문은 적응적 배경영상 모델링 방법을 이용한 불법주정차 무인단속시스템에 관한 것으로서, 적응적 가우시안 혼합 모델로 배경 영상을 모델링할 때, 이동 물체의 상황 변화에 따라 전역적으로 배경 영상을 업데이트하거나 국소적으로 배경 영상을 업데이트하는 방법에 대해 기술한다. 특히, 이동 물체가 배경 영상에 미치는 영향을 최소화하는 방법과 배경 영상을 정확하게 업데이트하기 위한 방법을 제안한다. 본 논문에서는 시스템의 구현을 통해 제안하는 시스템이 이동하고 있는 물체 또는 정지상태의 물체를 신속하고 정확하게 구분할 수 있음을 증명하였다.

Keywords

References

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