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Multicamera fusion for online analysis of structured processes

Published:27 May 2014Publication History

ABSTRACT

We propose a novel framework for online analysis of visual structured processes, using fusion from multiple cameras. Online recognition is performed through particle filters supported by hidden Markov models. We evaluate three fusion methods, an early fusion, a simple multiplication of the observation probabilities and a multi-stream one implying cross-stream coupling of observations and states. The performance is thoroughly evaluated under two complex visual behavior understanding scenarios: a visual process for table preparation in a kitchen and a real life manufacturing process in an industrial plant. The obtained results are compared and discussed.

References

  1. D. Arnaud, G. Simon, and A. Christophe. On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10(3):197--208, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. S. Arulampalam, S. Maskell, and N. Gordon. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2):174--188, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. K. Bernardin, T. Gehrig, and R. Stiefelhagen. Multimodal technologies for perception of humans. chapter Multi-level Particle Filter Fusion of Features and Cues for Audio-Visual Person Tracking, pages 70--81. Springer-Verlag, Berlin, Heidelberg, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Y. Chen and Y. Rui. Real-time speaker tracking using particle filter sensor fusion. Proceedings of the IEEE, 92(3): 485--494, mar 2004.Google ScholarGoogle ScholarCross RefCross Ref
  5. S. Eickeler, A. Kosmala, and G. Rigoll. Hidden markov model based continuous online gesture recognition. In In Int. Conference on Pattern Recognition (ICPR, pages 1206--1208, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. H. Fei. A hybrid hmm/particle filter framework for non-rigid hand motion recognition. In Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on, volume 5, pages V -- 889--92 vol.5, may 2004.Google ScholarGoogle Scholar
  7. S. Fine, Y. Singer, and N. Tishby. The hierarchical hidden markov model: Analysis and applications. Machine Learning, 32(1):41--62, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. G. Gravier, G. Potamianos, and C. Neti. Asynchrony modeling for audio-visual speech recognition. In Proceedings of the second international conference on Human Language Technology Research, HLT '02, pages 1--6, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. D. Kosmopoulos and S. Chatzis. Robust visual behavior recognition. Signal Processing Magazine, IEEE, 27(5):34--45, sep. 2010.Google ScholarGoogle ScholarCross RefCross Ref
  10. D. Kosmopoulos, A. Voulodimos, and T. Varvarigou. Robust human behavior modeling from multiple cameras. In Pattern Recognition (ICPR), 2010 20th 697 International Conference on, pages 3575--3578, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. I. Kosmopoulos, N. D. Doulamis, and A. S. Voulodimos. Bayesian filter based behavior recognition in workflows allowing for user feedback. Computer Vision and Image Understanding, 116(3):422--434, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. F. Lv and R. Nevatia. Recognition and segmentation of 3-d human action using hmm and multi-class adaboost. In ECCV06, pages IV: 359--372, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Nefian, L. Liang, X. Pi, L. Xiaoxiang, C. Mao, and K. Murphy. A coupled HMM for audio-visual speech recognition. In Acoustics, Speech, and Signal Processing, 2002. Proceedings. (ICASSP '02). IEEE International Conference on, volume 2, pages 2013--2016, 2002.Google ScholarGoogle Scholar
  14. N. Oliver, A. Garg, and E. Horvitz. Layered representations for learning and inferring office activity from multiple sensory channels. Comput. Vis. Image Underst., 96(2):163--180, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. N. Padoy, D. Mateus, D. Weinland, M.-O. Berger, and N. Navab. Workflow Monitoring based on 3D Motion Features. In Workshop on Video-Oriented Object and Event Classification in Conjunction with ICCV 2009, pages 585--592, Kyoto Japan, 2009. IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  16. L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257--286, 1989.Google ScholarGoogle ScholarCross RefCross Ref
  17. D. G. Stork and M. E. Hennecke. Speech reading by humans and machines. In NATO ASI Series F, volume 150. Springer Verlag, 1996.Google ScholarGoogle ScholarCross RefCross Ref
  18. M. Tenorth, J. Bandouch, and M. Beetz. The TUM Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition. In IEEE Int. Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences (THEMIS). In conjunction with ICCV2009, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  19. C. Vogler and D. Metaxas. A framework for recognizing the simultaneous aspects of American sign language. Computer Vision and Image Understanding, 81(358--384), 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. A. Voulodimos, D. Kosmopoulos, G. Vasileiou, E. Sardis, V. Anagnostopoulos, C. Lalos, A. Doulamis, and T. Varvarigou. A threefold dataset for activity and workflow recognition in complex industrial environments. MultiMedia, IEEE, 19(3):42--52, July 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. X. Xiaoling and L. Layuan. Real time analysis of situation events for intelligent surveillance. In Computational Intelligence and Design, 2008. ISCID '08. International Symposium on, volume 2, pages 122--125, oct. 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Z. Zeng, J. Tu, B. M. P. Jr., and T. S. Huang. Audio--visual affective expression recognition through multistream fused HMM. IEEE Trans. Multimedia, 10(4):570--577, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. D. Zhang, X. Ning, and X. Liu. Smc method for online prediction in hidden markov models. Kybernetes, 38(10):1819--1827, 2009.Google ScholarGoogle ScholarCross RefCross Ref

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      • Published in

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        PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments
        May 2014
        408 pages
        ISBN:9781450327466
        DOI:10.1145/2674396

        Copyright © 2014 ACM

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        • Published: 27 May 2014

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