한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제17권1호
  • /
  • Pages.15-25
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  • 2014
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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방범용 CCTV를 위한 배회행위 탐지 솔루션

Loitering Detection Solution for CCTV Security System

  • 강주형 (한밭대학교 제어계측공학과) ;
  • 곽수영 (한밭대학교 전자.제어공학과)
  • 투고 : 2013.07.17
  • 심사 : 2013.11.22
  • 발행 : 2014.01.31
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초록

본 논문에서는 지능형 감시 시스템을 위해 공간적 확률 분포와 방향 서술자를 이용하여 다양한 배회행위를 검출하는 방법을 제안한다. 적응적 배경 모델링 기법을 이용하여 움직이는 객체를 검출하고, 검출된 객체로부터 움직임의 정보를 추출한다. 추출된 객체의 움직임 정보는 이동 궤적과 방향에 대해 특징벡터를 생성한다. 생성된 특징벡터는 k-Nearest Neighbor를 통해 최종적으로 배회행위를 검출하게 된다. 제안한 방법을 실내외 다양한 환경에서 테스트하여 배회 행위를 검출하는 결과를 나타내었으며 이는 실시간으로 검출되는 것을 확인하였다.

In this paper, we propose a loitering detection using trajectory probability distribution and local direction descriptor for intelligent surveillance system. We use a background modeling method for detecting moving object and extract the motion features from each moving object for making feature vectors. After that, we detect the loitering behavior person using K-Nearest Neighbor classifier. We test the proposed method in real world environment and it can achieve real time and robust detection results.

키워드

참고문헌

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피인용 문헌

  1. Introduction of Real-Time Video Surveillance System Using UAV 2016, https://doi.org/10.12720/jcm.11.2.213-220
  2. CCTV Based Gender Classification Using a Convolutional Neural Networks vol.19, pp.12, 2016, https://doi.org/10.9717/kmms.2016.19.12.1943
  3. Versatile loitering detection based on non-verbal cues using dense trajectory descriptors pp.1573-7721, 2019, https://doi.org/10.1007/s11042-018-6618-9
  4. 지능 영상 감시를 위한 흑백 영상 데이터에서의 효과적인 이동 투영 음영 제거 vol.17, pp.4, 2014, https://doi.org/10.9717/kmms.2014.17.4.420
  5. 유효영상 획득을 위한 무인기 영상감시의 실시간 위치분석과 무선전송 기술에 관한 연구 vol.18, pp.9, 2014, https://doi.org/10.9717/kmms.2015.18.9.1047
  6. Loitering Detection Based on Pedestrian Activity Area Classification vol.9, pp.9, 2019, https://doi.org/10.3390/app9091866
  7. Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks vol.9, pp.2, 2021, https://doi.org/10.3390/computation9020024
  8. 객체 탐지와 행동인식을 이용한 영상내의 비정상적인 상황 탐지 네트워크 vol.24, pp.2, 2014, https://doi.org/10.9717/kmms.2020.24.2.186