Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Loitering Detection Solution for CCTV Security System

방범용 CCTV를 위한 배회행위 탐지 솔루션

  • 강주형 (한밭대학교 제어계측공학과) ;
  • 곽수영 (한밭대학교 전자.제어공학과)
  • Received : 2013.07.17
  • Accepted : 2013.11.22
  • Published : 2014.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

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.

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.

Keywords

References

  1. 정치윤, 한종욱, "지능형 영상분석 이벤트 탐지 기술동향," 전자통신동향분석, 제27권 제4호, pp. 114-122, 2012.
  2. 이승원, 김태경, 유장희, 백준기, "지능형 비디오 분석을 위한 적응적 배경 생성 기반의 이상 행위 검출," 대한전자공학회논문지, 제48권, 제1호, pp. 111-121, 2011.
  3. Chung-Hsien Huang, Ming-Yu Shih, Yi-Ta Wu and Jau-Hong Kao, "Loitering Detection Using Bayesian Appearance Tracker and List of Visitors," Advances in Multimedia Information Processing, PCM'08, LNCS 5353, pp. 906-910, 2008.
  4. Thi Thi Zin, Pyke Tin, and Takashi Toriu, "A Markov Random Walk Model for Loitering People Detection," International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 680-683, 2010.
  5. 박은수, 김학일, "그림자 제가와 색도 히스토그램 비교를 이용한 배회행위검출," 한국정보보호학회논문지, 제21권, 제6호, pp. 159-169, 2011.
  6. Nathaiel D. Bird, Osama Masoud, Nikolaos P. Papanikolopoulos, and Aaron Isaac, "Detection of Loitering Individuals in Public Transportation Areas," IEEE Transactions on Intelligent Transportation Systems, Vol. 6, No. 2, pp. 167-177, 2005. https://doi.org/10.1109/TITS.2005.848370
  7. Mohannad Elhamod and Martin D. Levine, "Automated Real-Time Detection of Potentially Suspicious Behavior in Public Transport Areas," IEEE Transactions on Intelligent Transportation Systems, Vol. 14, No. 2, pp. 688-699, 2013. https://doi.org/10.1109/TITS.2012.2228640
  8. Aergio A. Velastin, Boghossian A. Boghossian, Benny Ping Lai Lo, Jie Sun, and Maria Alicia Vicencio-Silva, "PRISMATICA: Toward Ambient Intelligence in Public Transport Environments," IEEE Systems. Man. and Cybernetics, Vol. 35, No. 1, pp. 164-182, 2005.
  9. A. Singh, S. Sawan, M. Hanmandlu, V.K. Madasu, and B.C. Lovell, "An Abandoned Object Detection System Based on Dual Background Segmentation," Proc. Int. Conf. Advanced Video, Signal Based Surveillance, pp. 352-357, 2009.
  10. G.G. Lee, J.J. Kim, and W.Y. Kim, "A Fast Background Subtraction Method Robust to High Traffic and Rapid Illumination Changes," Journal of Korea Multimedia Society, Vol. 13, No. 3, pp. 417-429, 2010.
  11. Arnold Wiliem, Vamsi Madasu, Wageeh Boles, and Prasad Yarlagadda, "Detecting Uncommon Trajectories," Digital Image Computing. Techniques and Applications, pp. 398-404, 2008.
  12. Hua Yang, Yihua Cao, Shuang Wu, and Weiyao Lin, "Abnormal Crowd Behavior Detection Based on Local Pressure Model," Signal and Imformation Processing Association Annual Summit and Conference, pp. 1-4, 2012.
  13. Myo Thida, How-Lung Eng, and Paolo Remagnino, "Laplacian Eigenmap with Temporal Constraints for Local Abnormality Detection in Crowded Scenes," IEEE Transactions on Cybernetics, Vol 43, No. 6, pp. 1-10, 2013. https://doi.org/10.1109/TCYB.2013.2267631
  14. Sunil Kumar Kopparapu and M Satish, "Identifying Optimal Gaussian Filter for Gaussian Noise Removal," 2011 Third National Conference on Image Processing and Graphics, pp. 126-129, 2011.
  15. Tian Wang and Hichem Snoussi, "Histograms of Optical Flow Orientation for Visual Abnormal Events Detection," 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, pp. 13-18, 2012.
  16. G.T. Bea, S.Y. Kwak, and H.R. Byun, "Abnormal motion detection using dominant motion analysis," Workshop on Image Processing and Image Under-Standing, 2010.
  17. PETS 2007 Benchmark data, http://www.hitech-projects.com/euprojects/ cantata/datasets_ cantata/dataset.html, 2007.
  18. D. Simon, "Kalman Filtering with State Constraints: A Survey of Linear and Nonlinear Algorithms," Control Theory and Applications. IET, Vol. 4, No. 8, pp. 1303-1318, 2010. https://doi.org/10.1049/iet-cta.2009.0032

Cited by

  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