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일반 CCTV 기반 차량 번호판 인식 시스템

License Plate Recognition System based on Normal CCTV

  • 장지웅 (서울과학기술대학교, 일반대학원 미디어공학과) ;
  • 박구만 (서울과학기술대학교, 전자IT미디어공학과)
  • Woong, Jang Ji (Dept. of Media IT Engineering, The Graduate School, Seoul National University of Science and Technology) ;
  • Man, Park Goo (Dept. of Electronics IT Media Engineering, Seoul National University of Science and Technology)
  • 투고 : 2017.05.11
  • 심사 : 2017.07.05
  • 발행 : 2017.08.25

초록

본 논문에서는 일반 도로상에 설치된 CCTV 영상으로부터 차량 검출과 번호판을 인식하는 시스템을 제안하였다. 본 시스템의 환경은 일반 도로 환경에서 영상을 취득하기 때문에 기존의 차량 진출입 시스템에 적용되는 안정적인 조건이 주어지지 않으며 입력 영상이 왜곡되고 해상도가 불규칙적이다. 동시에 입력 영상의 시야각이 넓어 연산량이 높고 번호판의 인식 정확도가 떨어지기 쉽다. 본 논문에서는 별도의 입력 제어 장치 없이 차량을 검출하고, 번호판 검출 및 인식이 가능한 향상된 방법을 제안하였다. HOG 특징 기술자를 기반으로 차량 및 번호판을 검출하고, k-NN 알고리즘을 사용하여 번호판 내부 문자의 인식을 수행하였다. CCTV에서 45m 이상 떨어진 장소의 도로를 실험 환경으로 설정하고, 육안으로 번호판을 식별할 수 있는 진입 차량에 대한 실험을 진행하였으며 실험을 통하여 제안 방식의 우수한 결과를 확인하였다.

This Paper proposes a vehicle detection system and a license plate recognition system from CCTV images installed on public roads. Since the environment of this system acquires the image in the general road environment, the stable condition applied to the existing vehicle entry / exit system is not given, and the input image is distorted and the resolution is irregular. At the same time, the viewing angle of the input image is more wide, so that the computation load is high and the recognition accuracy of the plate is likely to be lowered. In this paper, we propose an improved method to detect and recognize a license plate without a separate input control devices. The vehicle and license plate were detected based on the HOG feature descriptor, and the characters inside the license plate were recognized using the k-NN algorithm. Experimental environment was set up for the roads more than 45m away from the CCTV, Experiments were carried out on an entry vehicle capable of visually identifying license plate and Experimental results show good results of the proposed method.

키워드

참고문헌

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피인용 문헌

  1. 고무타이어의 음각 문자 인식 향상에 관한 연구 vol.9, pp.10, 2017, https://doi.org/10.15207/jkcs.2018.9.10.007