Online recognition and segmentation for time-series motion with HMM and conceptual relation of actions

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Volumn , Issue , 2005, Pages 3864-3870

  Mori, Taketoshi ^a   Nejigane, Yu ^a   Shimosaka, Masamichi ^a   Segawa, Yushi ^a   Harada, Tatsuya ^a   Sato, Tomomasa ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Action recognition; Hidden markov model; Motion capture; Segmentation; Support vector machine

Indexed keywords

IMAGE SEGMENTATION; INTELLIGENT ROBOTS; SUPPORT VECTOR MACHINES; TIME SERIES; TRELLIS CODES;

ACTION RECOGNITION; ACTION RECOGNITION ALGORITHMS; CLASSIFICATION TECHNIQUE; CONCEPTUAL RELATIONS; HIDDEN MARKOV MODELS (HMMS); MOTION CAPTURE; ON-LINE RECOGNITION; SEGMENTATION SCHEME;

HIDDEN MARKOV MODELS;

EID: 48149103615     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2005.1545363     Document Type: Conference Paper

References (22)

1. J. Davis and A. Bobick. The representation and recognition of human movement using temporal templates. In Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 928-934, 1997.
2. J. Yamato, J. Ohya, and K. Ishii. Recognizing Human Action in Time- Sequential Images using Hidden Markov Model. In Proceedings of the 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 379-385, 1992.
3. T. Mori, K. Tsujioka, M. Shimosaka, and T. Sato. Human-like action recognition system using features extracted by human. In Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1214-1220, 2002. (Pubitemid 35437974)
4. T. Mori, M. Shimosaka, T. Harada, and T. Sato. Recognition of actions in daily life and its performance adjustment based on support vector learning. International Journal of Humanoid Robotics, 1(4):565-583, 2004.
5. T. Inamura, Y. Nakamura, H. Ezaki, and I. Toshima. Imitation and primitives symbol acquisition of humanoids by integrated mimesis loop. In Proceedings of the 2001 IEEE International Conference on Robotics and Automation, pages 4208-4213, 2001. (Pubitemid 32815517)
6. C. Bregler. Learning and recognizing human dynamics in video sequences. In Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 568- 574, 1997.
7. V. Pavlović, J. Rehg, and J. MacCormick. Learning swtiching linear models of human motion. In T. Leen, T. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 981-987, 2001.
8. J. Kohlmorgen and S. Lemm. A dynamic HMM for on-line segmentation of sequential data. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, 2002.
9. X. Ge. Segmental semi-Markov models and applications to sequence analysis. PhD thesis, University of California Irvine, 2002.
10. T. Mori, Y. Segawa, M. Shimosaka, and T. Sato. Hierarchical recognition of daily human actions based on continuous hidden Markov models. In Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pages 779-784, 2004.
11. R. Duda, P. Hart, and D. Stork. Pattern classification (2nd ed.). Wiley Interscience, 2000.
12. C. Williams and D. Barber. Bayesian classification with Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1342-1351, 1998. (Pubitemid 128741378)
13. B. Schölkopf and A. Smola. Learning with kernels. MIT Press, 2002.
14. D. Tax, A. Ypma, and R. Duin. Outlier detection using classifier instability. In Proceedings of the Seventh International Workshop on Structural and Syntactic Pattern Recognition, 1998.
15. B. Schölkopf, R. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt. Support vector method for novelty detection. In S. Solla, T. Leen, and K. M̈uller, editors, Advances in Neural Information Processing Systems 12, pages 582-588, 2000.
16. S. Romdhani, P. Torr, B. Schölkopf, and A. Blake. Efficient face detection by a cascaded support-vector machine expansion. Proceedings of The Royal Society A, 460(2501):3283-3297, 2004. (Pubitemid 40522420)
17. M. Tipping. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211-244, 2001. (Pubitemid 33687203)
18. J. Platt. Probabilities for SV machines. In A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 61-74, 1999.
19. L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. In Proceedings of the IEEE, volume 77, pages 257-285, 1989.
20. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B(Methodological), 39(1):1-38, 1977.
21. T. Mori, M. Shimosaka, and K. Tsujioka. ICS action database. http://www.ics.t.u-tokyo.ac.jp/action/.
22. M. Shimosaka, T. Mori, T. Harada, and T. Sato. Marginalized bags of vectors kernels on switching linear dynamics for online action recognition. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pages 3083-3088, 2005.