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A novel hybrid approach for human silhouette segmentation

Published:01 July 2015Publication History

ABSTRACT

In this work we propose a novel algorithm for human silhouette segmentation, which combines characteristics from a number of well established and state of the art algorithms, such as the Gaussian mixture models, the Self Organizing Maps and the Illumination Sensitive method. The proposed algorithm is evaluated against user-defined ground truth segmentation for two different types of indoor video sequences, one of which was obtained by a hemispheric camera. The behavior of the algorithm with respect to its controlling parameters is investigated and its computational burden is studied.

References

  1. 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
  2. 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, 1337--1442. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. McFarlane, N., and Schofield, C. 1995. Segmentation and tracking of piglets in images. Mach. Vision Appl. 8, 3, (May. 1995), 187--193.Google ScholarGoogle Scholar
  4. 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
  5. Bouwmans, T., El Baf, F., Vachon, B. 2008. Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey. Recent Patents on Computer Science 1, 3, 219--237.Google ScholarGoogle ScholarCross RefCross Ref
  6. Ha, J.-E., and Lee, W.-H. 2010. Foreground objects detection using multiple difference images. Opt. Eng., vol. 49, no. 4, p. 047201.Google ScholarGoogle ScholarCross RefCross Ref
  7. Stauffer, C., Grimson, W. 1999. Adaptive background mixture models for real-time tracking. In Proc IEEE Conf on Comp Vision and Patt. Recog. (CVPR 1999); 246--252.Google ScholarGoogle ScholarCross RefCross Ref
  8. KaewTraKulPong, P., and Bowden, R. 2001. An Improved Adaptive Background Mixture Model for Real time Tracking with Shadow Detection. In Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01. Sept 2001: Computer Vision and Distributed Processing, Kluwer Academic Publishers.Google ScholarGoogle Scholar
  9. Maddalena, L., and Petrosino, A. 2008. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. In IEEE Transactions On Image Processing, Vol. 17, No. 7, 1168--1177. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Cheng, F. C., Huang, S. C., and Ruan, S. J. 2011. Implementation of Illumination-Sensitive Background Modeling Approach for Accurate Moving Object Detection. In IEEE Trans. on Boardcasting, vol. 57, no. 4, 794--801.Google ScholarGoogle ScholarCross RefCross Ref

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      PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments
      July 2015
      526 pages
      ISBN:9781450334525
      DOI:10.1145/2769493

      Copyright © 2015 ACM

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      Publication History

      • Published: 1 July 2015

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