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Markerless Articulated Human Body Tracking for Gait Analysis and Recognition

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We present a particle swarm optimization (PSO) based system for markerless full body motion tracking. The fitness function is smoothed in an annealing scheme and then quantized. In this manner we extract a pool of candidate best particles. The swarm of particles selects a global best from such a pool of the particles to force the PSO the jump out of stagnation. Experiments on 4-camera datasets demonstrate the accuracy of our method on image sequences with walking persons. The system was evaluated using ground-truth data from a marker-based motion capture system by Vicon. We compared the joint motions and the distances between ankles, which were extracted using both systems. Thanks to the high precision of the markerless motion estimation, the curves illustrating the distances between ankles overlap considerably in almost all frames of the image sequences.
Rocznik
Strony
267--281
Opis fizyczny
Bibliogr. 24 poz., il., wykr.
Twórcy
  • Polish-Japanese Institute of Information Technology, Aleja Legionów 2, 41-902 Bytom, Poland
  • Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
autor
  • Polish-Japanese Institute of Information Technology, Aleja Legionów 2, 41-902 Bytom, Poland
  • Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
  • Polish-Japanese Institute of Information Technology, Aleja Legionów 2, 41-902 Bytom, Poland
  • Silesian University of Technology, Akademicka 16, 41-100 Gliwice, Poland
autor
  • Polish-Japanese Institute of Information Technology, Aleja Legionów 2, 41-902 Bytom, Poland
  • Silesian University of Technology, Akademicka 16, 41-100 Gliwice, Poland
Bibliografia
  • [1] Johansson G.: Visual perception of biological motion and a model for its analysis, Perception and Psychophysics, vol. 14, 201-211, 1973.
  • [2] Sakoe H., Chiba S.: Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans, on Acoustics, Speech and Signal Processing, 26 (1), pp.4349, 1978.
  • [3] Dyer S., Martin J., Zulauf J.: Motion Capture White Paper, SGI, 1995, on-line at ftp://ftp.sgi.com/sgi/A%7CW/jam/mocap/MoCapWP_v2.0.html.
  • [4] Kennedy J., Eberhart R. C.: Particle swarm optimization, Proc. of IEEE Int. Conf. on Neural Networks, Piscataway, NJ, pp. 1942-1948, 1995.
  • [5] Cedras C., Shah M.: Motion-based recognition a survey, Image and Vision Computing, vol. 13, 129-155, 1995.
  • [6] Isard M., Blake A.: Contour tracking by stochastic propagation of conditional density, In Proc. of the 4th European Conference on Computer Vision, Springer-Verlag, Cambridge, London, UK, 343-356, 1996.
  • [7] Doucet A., Godsill S., Andrieu Ch.: On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, vol. 10, no. 3, 197-208, 2000.
  • [8] Tanawongsuwan R., Bobick A.: Gait recognition from time-normalized joint-angle trajectories in the walking plane, Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2001, vol. 2, pp. II-726-731, 2001.
  • [9] Clerc M., Kennedy J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. on Evolutionary Computation 6 (1), 58-73, 2002.
  • [10] Urtasun R., Fua P.: 3D tracking for gait characterization and recognition. In Proc. of the Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition, IEEE Computer Society, Washington, DC, USA, 17-22, 2004.
  • [11] Boulgouris N. V., Hatzinakos D., Plataniotis K. N.: Gait recognition: a challenging signal processing technology for biometric identification, Signal Processing Magazine, IEEE, vol.22, no. 6, pp. 78-90, Nov. 2005.
  • [12] Saboune J., Charpillet F.: Markerless human motion capture for gait analysis, Clinical Orthopaedics and Related Research, Springer, 2005.
  • [13] Moeslund T. B., Hilton A., Krüger V.: A survey of advances in vision-based human motion capture and analysis, Comput. Vis. Image Underst., vol. 104, no. 2-3, 90-126, Nov. 2006.
  • [14] Mündermann L., Corazza S., Andriacchi T. P.: The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications, J. Neuroengineering Rehabil., vol. 3, no. 6, 2006.
  • [15] Nixon M. S., Carter J. N.: Automatic Recognition by Gait, Proc. of the IEEE, vol. 94, no. 11, pp. 2013-2024, 2006.
  • [16] Poppe R.: Vision-based human motion analysis: An overview, Comput. Vis. Image Underst., vol. 108, no. 1-2, 4-18, 2007.
  • [17] Barris S., Button C.: A review of vision-based motion analysis in sport, Sports Medicine, vol. 38, no. 12, 1025-1043, 2008.
  • [18] Zhou H., Hu H.: Human motion tracking for rehabilitation - A survey, Biomedical Signal Proc. and Control (3):118, 2008.
  • [19] Quah C. K., Koh M., Ong A., Seah H. S., Gagalowicz A.: Video-based Motion Capture for Measuring Human Movement, [in:] Digital Sports for Performance Enhancement and Competitive Evolution: Intelligent Gaming Technologies, Information Science Publishing, 209-228, 2009.
  • [20] Bodor R., Drenner A., Febr D., Masoud O., Papanikolopoulos N.: View-independent human motion classification using image-based reconstruction, Image Vision Comput., vol. 27, no. 8, 1194-1206, 2009.
  • [21] Kwolek B.: Multiple views based human motion tracking in surveillance videos, In Proc. of 8th IEEE Int. Conf. on Advanced Video and Signal-Based Surveillance, pp. 492-497, 2011.
  • [22] Sigal L., Fleet D. J, Troje N. F., Livne M.: Human attributes from 3D pose tracking, In Proc. of the 11th European Conf. on Computer Vision, vol. III, Springer-Verlag, Berlin, Heidelberg, 243-257, 2010.
  • [23] Kwolek B., Krzeszowski T., Wojciechowski K.: Swarm intelligence based searching schemes for articulated 3D body motion tracking. Int. Conf. on Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science, vol. 6915, Springer-Verlag, pp. 115-126, 2011.
  • [24] Switoński A., Polański A., Wojciechowski K.: Human Identification Based on Gait Paths, Int. Conf. on Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science, vol. 6915, Springer-Verlag, pp. 531-542, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2be8adfd-f5f4-4d2c-be06-c2d6610be819
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