방송공학회논문지 (Journal of Broadcast Engineering)

  • 제10권2호
  • /
  • Pages.156-165
  • /
  • 2005
  • /
  • 1226-7953(pISSN)
  • /
  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
권호 표지

Adaptive Background Subtraction Algorithm with Auto Brightness Control for Consumer-type Cameras

  • Thongkamwitoon T. (Department of Electrical Engineering Faculty of Engineering Chulalongkorn University) ;
  • Aramvith S. (Department of Electrical Engineering Faculty of Engineering Chulalongkorn University) ;
  • Chalidabhongse T. H. (Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang)
  • 발행 : 2005.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents a new auto brighoess control algorithm fur adaptive background subtraction. The algorithm is designed to cope with the problem of auto-brightness adjustment feature of consumer-type cameras. The experimental results show the proposed method improves performance of the classification. This will be beneficial to many computer vision applications in term of reducing the cost of implementation and making them more available to the mass consumer market.

키워드

참고문헌

  1. C.R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, 'Ptinder: Real-time tracking of the human body,' IEEE Transactions on PAMI, Vol. 19, no. 7, pp. 780-785, 1997 https://doi.org/10.1109/34.598236
  2. I. Haritaoglu, D. Harwood, and L.S. Davis. 'W4: Realtime surveillance of people and their activities'. IEEE Trans. on PAMI, 2000
  3. T. Horprasert, D. Harwood, and L.S. Davis, 'A statistical approach for real-time robust background subtraction and shadow detection,' IEEE Frame-Rate Applications Workshop, Kerkyra, Greece, 1999
  4. K. Toyama, B. Brumitt J. Krumm, and B. Meyers. 'Wallflower: Principles and practicle of background maintenance'. In Proceedings of International Conference on Computer Vision, 1999
  5. M. Harville, 'A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models,' European Conf. Computer Vision, Vol. 3, pp. 543-560, 2002
  6. C. Stauffer and W.E.L. Grimson, 'Adaptive background mixture models for real-time tracking,' Int. Conf. Computer Vision and Pattern Recognition, Vol. 2, pp. 246-252, 1999
  7. J. Shingu, Y. kameda, M. Mukunoki, ' Image-based Dynamic Lighting Control', Proceedings of International Workshop on Pattern Recognition and Understanding for Visual Information Media, 2002
  8. T. Thongkamwitoon, S. Aramvith and T.H. Chalidabongse, 'An Adaptive Real-time Background Subtraction and Moving Shadows Detection,' Proceeding of International Conference on Multimedia and Expo 2004 (ICME 2004)., Vol. 2, pp. 1459-1462. 2004