방송공학회논문지 (Journal of Broadcast Engineering)

  • 제22권6호
  • /
  • Pages.774-784
  • /
  • 2017
  • /
  • 1226-7953(pISSN)
  • /
  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
권호 표지
DOI QR코드

DOI QR Code

카메라 비전 센서를 활용하는 실시간 사람 점유 검출

Real-time People Occupancy Detection by Camera Vision Sensor

  • 길종인 (강원대학교 컴퓨터정보통신공학과) ;
  • 김만배 (강원대학교 컴퓨터정보통신공학과)
  • Gil, Jong In (Dept. of Computer and Communications Engineering, Kangwon National University) ;
  • Kim, Manbae (Dept. of Computer and Communications Engineering, Kangwon National University)
  • 투고 : 2017.07.25
  • 심사 : 2017.09.11
  • 발행 : 2017.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

빌딩, 집에 설치되어 있는 점유센서는 사람이 없으면 소등하고, 반대이면 점등한다. 현재는 주요 센서로 PIR(pyroelectric infra-red)이 널리 사용되고 있다. 최근에 비전 카메라 센서를 이용하여 사람 점유를 검출하는 연구가 진행되고 있다. 카메라 센서는 정지된 사람을 검출할 수 없는 PIR의 단점을 극복할 수 있는 장점이 있다. 또한 카메라 센서는 사람의 행위 분석, 사람 트랙킹 등 PIR이 제공할 수 없는 기능을 가지기 때문에 향후 가격 대비 성능이 만족되면 PIR을 대체할 것으로 기대된다. 본 논문에서는 PIR 센서의 단점을 극복하기 위해서 카메라를 이용한 점유센서 기법을 제안한다. 제안 방법은 트랙킹, 인식, 검출의 3가지 단계로 구성되어 점유검출의 효율성을 높힌다. 실시간 처리도 중요한 성능이므로 처리 속도가 향상되도록 설계되었다. 비디오 프레임은 USB로 15fps로 입력되는데, 본 소프트웨어는 14.5fps로 처리한다. 점유 성능 검증에서는 82%의 정확도를 얻었다.

Occupancy sensors installed in buildings and households turn off the light if the space is vacant. Currently PIR (pyroelectric infra-red) motion sensors have been utilized. Recently, the researches using camera sensors have been carried out in order to overcome the demerit of PIR that can not detect static people. If the tradeoff of cost and performance is satisfied, the camera sensors are expected to replace the current PIRs. In this paper, we propose vision sensor-based occupancy detection being composed of tracking, recognition and detection. Our softeware is designed to meet the real-time processing. In experiments, 14.5fps is achieved at 15fps USB input. Also, the detection accuracy reached 82.0%.

키워드

참고문헌

  1. P. Liu, S. Nguang, and A. Partridge, "Occupancy inference using pyroelectric infrared sensors through hidden markov model", IEEE Sensors Journal, 16(4), Feb. 2016.
  2. F. Wahl, M. Milenkovic, and O. Amft, "A distributed PIR-based approach for estimating people count in office environments", IEEE Conf. on Computational Science and Engineering, 2012.
  3. Y. Benezeth, H. Laurent, B. Emile, and C. Rosenberger, "Towards a sensor for detecting human presence and characterizing activity", Energy and Buildings, 43, 2011.
  4. J. Han and B. Bhanu, "Fusion of color and infrared video for moving human detection", Pattern Recognition, 40, 2007.
  5. S. Nakashima, Y. KItazono, L. Zhang, and S. Serikawa. “Development of privacy-preserving sensor for person detection,” Procedia - Social and Behavioral Sciences, 2, 2010.
  6. I. Amin, A. Taylor, F. Junejo, A. Al-Habaibeh, and R. Parkin, "Automated people-counting by using low-resolution infrared and visual cameras", Measurement, 41, 2008.
  7. A. Bobick and J. Davis, "The recognition of human movement using temporal templates," IEEE Trans. Pattern Recognition and Pattern Analysis, Vol. 23, No. 3, Mar. 2001.
  8. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models", IEEE Trans. Pattern Recognition and Pattern Analysis, Vol. 32, No, 9, Sep. 2010.
  9. C. Stauffer and W. Grimson, "Adaptive background mixture models for real-time tracking," In Proceedings Computer Vision and Pattern Recognition, pp. 246-252, 1999.
  10. J. Guo, Y. Liu, C. Hsia, M. Shih and C. Hsu, "Hierarchical method for foreground detection using code book model," IEEE Trans. Circuit Sys. Video Tech., Vol. 21, No. 6, June 2011.
  11. N. McFarlane and C. Schofield, "Segmentation and tracking of piglets in image," Machine Vision and Applications, 8:187-193, 1995. https://doi.org/10.1007/BF01215814
  12. T. Tian, R. Schmidt, H. Liu, A. Hampapur, and M. T. Sun, "Robust Detection of Abandoned and Removed objects in complex surveillance videos," IEEE Trans. Sym. Man and Cybernetics - Part C: Applications and Reviews, Vol. 41, No. 5. pp. 565-577, Sep. 2011. https://doi.org/10.1109/TSMCC.2010.2065803
  13. A. Sanin, C. Sanderson, and B. Lovell, "Shadow Detection: A Survey and Comparative Evaluation of Recent Methods", Pattern Recognition, Vol. 45, Issue 4, pp. 1684-1695, Apr. 2012. https://doi.org/10.1016/j.patcog.2011.10.001
  14. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking", IEEE Trans. Pattern Recognition and Pattern Analysis, Vol. 25, No. 5, May 2003.
  15. S. Tand, M. Andriluka, B. Schiele, "Detection and tracking of occluded people", Int J. Computer Vision, Nov. 2013.
  16. H. Chandel and S. Vatta, "Occlusion Detection and Handling: Review", Int. J. of Computer Applications, Vol. 120, No. 10, June 2015.
  17. J. Foley, Computer Graphics: Principles Practice, Addison-Wesley, 1994.