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Near real-time human silhouette and movement detection in indoor environments using fixed cameras

Published:06 June 2012Publication History

ABSTRACT

The automated human behavior modeling is highly desired in the context of an assistive environment. In this paper, we describe a software video processing and analysis system to assist the near real time detection of human activity. The video data are acquired indoors from fixed cameras in the living environment. The proposed system uses image-processing techniques to segment the human figure from the background, suppress the appearance of its shadow and detect the path and velocity of its motion. Detail performance measurements of the proposed algorithm are given in terms of execution time. Initial results are presented for a small number of video sequences.

References

  1. Albanese, M., Chellappa, R., Cuntoor, N., Moscato, V., Picariello, A., Subrahmanian, V. S., Udrea, O. 2010, PADS: A Probabilistic Activity Detection Framework for Video Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 12 (Dec. 2010), 2246--2261. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Murayama, H. and Yamada, K. 2010, Detection of unusual human activity based on sequence of actions with MHI and CDP, In Proceedings TENCON 2010 - 2010 IEEE Region 10 Conference, (Nov. 2010), 1663--1667, 21--24.Google ScholarGoogle ScholarCross RefCross Ref
  3. Takai, M. 2010, Detection of suspicious activity and estimate of risk from human behavior shot by surveillance camera, In Proceedings of the Second World Congress on Nature and Biologically Inspired Computing (NaBIC), 2010, pp.298--304, (15--17 Dec. 2010).Google ScholarGoogle ScholarCross RefCross Ref
  4. Zhongna Zhou, Xi Chen; Yu-Chia Chung, Zhihai He, Han, T. X.; Keller, J. M. 2008, Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring, Circuits and Systems for Video Technology, IEEE Circuits and Systems for Video Technology, 18, 11, 1489--1498. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Nait-Charif, H. McKenna, S. J. 2004, Activity summarisation and fall detection in a supportive home environment, In proceedings of the 17th International Conference on Pattern Recognition (ICPR) 2004, pp. 323--236, Aug. 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Miaou, S. --G., Sung, P.-H., Huang C. --Y. 2006, "A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information", In Proceedings of the 1st Transdisciplinary conference on Distributed Diagnosis and Home Healthcare, pp. 39--42, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  7. Doukas, C. N. and Maglogiannis, I. 2011, Emergency Fall Incidents Detection in Assisted Living Environments Utilizing Motion, Sound, and Visual Perceptual Components, IEEE Transactions on Information Technology in Biomedicine, 15, 2, (March 2011), 277--289. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Willems, J., Debard, G., Bonroy, B., Vanrumste, B., and Goedemé, T. 2009. How to detect human fall in video? An overview. In Proceedings of the positioning and contex-awareness international conference (Antwerp, Belgium, 28 May, 2009), POCA '09.Google ScholarGoogle Scholar
  9. Cucchiara, R., Grana, C, Piccardi, M., and Prati A. 2003. Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 10, (2003), 1337--1442. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Wren, C., Azarhayejani, A., Darrell, T., and Pentland, A. P. 1997. Pfinder: real-time tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7, (October. 1997), 780--785. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Stauffer, C. and Grimson, W. E. L. 1999. Adaptive background mixture models for real-time tracking. In Proceedings of the conference on computer vision and pattern recognition (Ft. Collins, USA, June 23--25, 1999), CVPR '99. IEEE Computer Society, New York, NY, 246--252.Google ScholarGoogle Scholar
  12. Bouwmans T., Baf F. El and Vachon B., Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey, Recent Patents on Computer Science 1, 3 (2008) 219--237.Google ScholarGoogle Scholar
  13. McFarlane, N. J. B. and Schofield, C. P. 1995. Segmentation and tracking of piglets in images. MACH VISION APPL. 8, 3, (May. 1995), 187--193.Google ScholarGoogle Scholar
  14. Gonzalez, R. C., Woods, R. E., and Eddins, S. L. 2004. Digital Image Processing using MATLAB 1st edition. Prentice Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Cucchiara, R., Grana, C., Piccardi, M., Prati A., and Sirotti, S. 2001. Improving shadow suppression in moving object detection with HSV color. In Proceedings of the conferenece in intelligent transportation system, (August. 2001), 334--339.Google ScholarGoogle Scholar

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  1. Near real-time human silhouette and movement detection in indoor environments using fixed cameras

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      cover image ACM Other conferences
      PETRA '12: Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
      June 2012
      307 pages
      ISBN:9781450313001
      DOI:10.1145/2413097

      Copyright © 2012 ACM

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      Publication History

      • Published: 6 June 2012

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