Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Deep Learning-based Vehicle Anomaly Detection using Road CCTV Data

도로 CCTV 데이터를 활용한 딥러닝 기반 차량 이상 감지

  • Shin, Dong-Hoon (Data Mining Lab., Department of Computer Science, Kyonggi University) ;
  • Baek, Ji-Won (Data Mining Lab., Department of Computer Science, Kyonggi University) ;
  • Park, Roy C. (Department of Information and Communication Software Engineering, Sangji University) ;
  • Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
  • 신동훈 (경기대학교 컴퓨터과학과) ;
  • 백지원 (경기대학교 컴퓨터과학과) ;
  • 박찬홍 (상지대학교 정보통신소프트웨어공학과) ;
  • 정경용 (경기대학교 컴퓨터공학부)
  • Received : 2020.09.11
  • Accepted : 2021.02.20
  • Published : 2021.02.28
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Abstract

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

현대사회에서는 차량을 소유하는 사람들이 증가하면서 교통문제가 발생하고 있다. 특히 고속도로 교통사고 문제는 발생률이 낮지만 치사율은 높다. 따라서 차량의 이상을 탐지하는 기술이 연구되고 있다. 이 중에는 딥러닝을 이용한 차량 이상탐지 기술이 있다. 이는 사고 및 엔진고장으로 인한 정차차량 등의 차량 이상을 탐지한다. 그러나 도로에서 이상이 발생할 경우 운전자의 위치를 파악할 수 있어야 빠른 대처가 가능하다. 따라서 본 연구에서는 도로 CCTV 데이터를 활용한 딥러닝 기반 차량 이상 감지 방법을 제안한다. 제안하는 방법은 먼저 도로 CCTV 데이터를 전처리한다. 전처리는 배경 추출 알고리즘인 MOG2를 이용하여 배경과 전경을 분리한다. 전경은 변위가 존재하는 차량을 의미하며 도로 위에서 이상이 존재하는 차는 변위가 없어 배경으로 판단된다. 배경이 추출된 이미지는 이상을 탐지하기 위해 YOLOv4를 이용하여 객체를 탐지한다. 해당 차량은 이상이 있음으로 판단한다.

Keywords

References

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