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Recognition of actions in meeting videos using timed automata

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Konferencja
International Conference on Computer Vision and Graphics ICCVG 2006 (25-27.09.2006 ; Warsaw, Poland)
Języki publikacji
EN
Abstrakty
EN
This paper addresses the problem of action recognition in meeting videos. A declarative knowledge provided graphically by the user together with person positions extracted by a tracking algorithm are used to generate the data for recognition. The actions have been formally specified using timed automata. The specification was verified on the basis of simulation tests as well as an analysis. The tracking is accomplished using a particle filter built on cues such as color, gradient and shape.
Rocznik
Strony
577--584
Opis fizyczny
Bibliogr. 17 poz., tab., wykr.
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autor
autor
Bibliografia
  • [1] Alur R. and Dill D. L.: A theory of timed automata, Theoretical Computer Science, vol. 126, no. 2, 183-235, 1994.
  • [2] Heitmeyer C., Mandrioli D.: Formal methods for real-time computing, Wiley, 1996.
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  • [4] Davis J. W., Bobick A. F.: The representation and recognition of human movement using temporal templates, Computer Vision and Pattern Recognition, 928-935, 1997.
  • [5] Yacoob, T., Black M. J.: Parameterized modeling and recognition of activities, Int. Conf. on Computer Vision, 232-247, 1998.
  • [6] Comaniciu D., Ramesh V., Meer P.: Real-time tracking of non-rigid objects using mean shift, In Proc. of IEEE Conf. on Comp. Vision and Patt. Rec., 142-149, 2000.
  • [7] Doucet A., Godsill S., Andrieu Ch.: On sequential monte carlo sampling methods for Bayesian filtering, Statistics and Computing, vol. 10, 197-208, 2000.
  • [8] Ivanov Y. A., Bobick A. F.: Recognition of visual activities and interactions by stochastic parsing, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, 852-872, 2000.
  • [9] Madabhushi A., Aggarwal J. K.: Using head movement to recognize human activity, In Proc. of 15th Int. Conf. on Pattern Recognition, 698-701, 2000.
  • [10] Oliver N. M., Rosario B., Pentland A. P.: A Bayesian Computer Vision System for modeling human interactions, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, 831-843, 2000.
  • [11] Perez P., Hue C., Vermaak J., Gangnet M.: Color-based probabilistic tracking, European Conference on Computer Vision, 661-675, 2000.
  • [12] Galata A., Johnson N., Hogg D.: Learning variable length Markov Models of behaviour, Computer Vision and Image Understanding, vol. 81, no. 3, 398-413, 2001.
  • [13] Behrmann G., David A., Larsen K. G.: A tutorial on Uppaal, Aalborg University, Denmark, 2004.
  • [14] Kwolek B.: Stereovision-based head tracking using color and ellipse fitting in a particle filter, European Conference on Computer Vision, LNCS, vol. 3024, Springer Verlag, (4): 192-204, 2004.
  • [15] Kwolek B.: Action recognition in meeting videos using head trajectories and fuzzy color histogram, Informatica, vol. 29, 281-289, 2005.
  • [16] Shet V. D., Harwood D., Davis L. S.: VidMAP: video monitoring of activity with Prolog, Proc. of IEEE Conf. on Advanced Video and Signal Based Surveillance, 224-229, 2005.
  • [17] Fritsch J., Schmidt J., Kwolek B.: Kernel particle filter for real-time 3D body tracking in monocular color images, IEEE Int. Conf. on Face and Gesture Recognition, Southampton, UK, IEEE Computer Society Press, 564-567, 2006.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0026-0037
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