방송공학회논문지 (Journal of Broadcast Engineering)

  • 제23권5호
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  • Pages.642-655
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  • 2018
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  • 1226-7953(pISSN)
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  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
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점유 센서를 위한 합성곱 신경망과 자기 조직화 지도를 활용한 온라인 사람 추적

Online Human Tracking Based on Convolutional Neural Network and Self Organizing Map for Occupancy Sensors

  • 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 : 2018.07.06
  • 심사 : 2018.08.09
  • 발행 : 2018.09.30
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⟨ 이전 논문 다음 논문 ⟩

초록

빌딩, 집에 설치되어 있는 점유 센서는 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. 이동 및 정지된 사람의 추적은 카메라 점유 센서의 주요 기능이다. 본 논문에서는 합성곱 신경망 모델과 자기 조직화 지도를 활용한 온라인 사람 추적 기법을 제안한다. 오프라인에 모델을 학습시키기 위해서는 많은 수의 훈련 샘플이 필요하다. 이러한 문제를 해결하기 위해, 학습되지 않은 모델을 사용하고, 실험 영상으로부터 직접 훈련 샘플을 수집하여 모델을 갱신한다. 오버헤드 카메라로 실내에서 촬영한 영상을 이용하여, 제안 방법이 효과적으로 사람을 추적하고 있음을 실험을 통해 증명하였다.

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 cannot detect stationary people. The detection of moving and stationary people is a main functionality of the occupancy sensors. In this paper, we propose an on-line human occupancy tracking method using convolutional neural network (CNN) and self-organizing map. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. Using videos capurted from an overhead camera, experiments have validated that the proposed method effectively tracks human.

키워드

참고문헌

  1. P. Liu, S. Nguang, and A. Partridge, "Occupancy inference using pyro-electric 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. Kltazono, L. Zhang, and S. Serikawa, "Development of privacy-preserving sensor for person detection", Proceedia-Social and Behavioral Sciences, 2(1)n, 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. J. Gil, and M. Kim, "Real-time People Occupancy Detection by Camera Vision Sensor", Journal of Broadcast Engineering, 22(6), pp. 774-784, 2017. https://doi.org/10.5909/JBE.2017.22.6.774
  8. H. Li, Y. Li, and F. Porikli, "DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking", IEEE Trans. on Image Processing, Vol. 25, No. 4, pp. 1834-1848, April 2016. https://doi.org/10.1109/TIP.2015.2510583
  9. K. Zhang, Q. Liu, and M. Yang, "Robust Visual Tracking via Convolutional Networks Without Training", IEEE Trans. on Image Processing, Vol. 25, No. 4, pp. 1779-1792, April 2016. https://doi.org/10.1109/TIP.2016.2531283
  10. X. Zhou, L. Xie, P. Zhang, and Y. Zhang, "An Ensemble of Deep Neural Networks for Object Tracking", IEEE Conf. on Image Processing, pp. 843-847, 2014.
  11. T. Kohonen, "Self-organized formation of topologically correct feature maps", Biological cybernetics, 43(1), pp. 59-69, 1982. https://doi.org/10.1007/BF00337288
  12. X. Jia, H. Lu and M. H. Yang, "Visual Tracking via Adaptive Structural Local Sparse Appearance Model", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1822-1829, 2012.
  13. J. F. Henriques, R. Caseiro, P. Martin and J. Batista, "Exploiting the Circulant Structure of Tracking-by-Detection with Kernels", European Conf. on Computer Vision, pp. 702-715, 2012.
  14. K. Zhang, L. Zhang and M. H. Yang, "Real-time Compressive Tracking", European Conf. on Computer Vision, pp. 864-877, 2012.
  15. L. Sevilla-Lara and E. Learned-Miller, "Distribution Fields for Tracking", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1910-1917, 2012.
  16. D. A. Ross, J. Lim, R. S. Lin and M. H. Yang, "Incremental Learning for Robust Visual Tracking", International Journal of Computer Vision, Vol. 77, Issue 1-3, pp. 125-141, 2008. https://doi.org/10.1007/s11263-007-0075-7
  17. C. Bao, Y. Wu, H. Ling and H. Ji, "Real Time Robust L1 Tracking Using Accelerated Proximal Gradient Approach", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1830-1837, 2012.
  18. S. Oron, A. Bar-Hillel, D. Levi and S. Avidan, "Locally Orderless Tracking", International Journal of Computer Vision, Vol. 111, No. 2, pp. 213-228, 2015. https://doi.org/10.1007/s11263-014-0740-6
  19. T. Zhang, B. Ghanem, S. Liu and N. Ahuja, "Robust Visual Tracking via Multi-task Sparse Learning", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2042-2049, 2012.
  20. W. Zhong, H. Lu and MH. Yang, "Robust Object Tracking via Sparsity-based Collaborative Model", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1838-1845, 2012.