MOTION-BASED FEATURE CLUSTERING FOR ARTICULATED BODY TRACKING

Hildegard Kuehne, Annika Woerner

Abstract

The recovery of three dimensional structures from moving elements is one of the main abilities of the human perception system. It is mainly based on particularities of how we interpret moving features, especially on the enforcement of geometrical grouping and definition of relation between features. In this paper we evaluate how the human abilities of motion based feature clustering can be transferred to an algorithmic approach to determine the structure of a rigid or articulated body in an image sequence. It shows how to group sparse 3D motion features to structural clusters, describing the rigid elements of articulated body structures. The location and motion properties of sparse feature point clouds have been analyzed and it is shown that moving features can be clustered by their local and temporal properties without any additional image information. The assembly of these structural groups could allow the detection of a human body in an image as well as its pose estimation. So, such a clustering can establish a basis for a markerless reconstruction of articulated body structures as well as for human motion recognition by moving features.

References

  1. Aggarwal, J.K., Cai, Q., 1999. Human Motion Analysis: A Review. In Computer Vision and Image Understanding, Vol. 73, No. 3, pp. 428-440.
  2. Aggarwal, J.K., Cai, Q., Liao, W., Sabata, B., 1994. Articulated and elastic non-rigid motion: A review. In Proc. IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pp 2-14.
  3. Bouguet, J.-Y., 2002. Pyramidal implementation of the Lucas Kanade feature tracker, description of the algorithm. Technical report, Intel Corporation.
  4. Cedras, C., Shah, M. 1994. A Survey of Motion Analysis from Moving Light Displays. In IEEE Conf. on Computer Vision and Pattern Recognition, pp. 214- 221.
  5. Corazza, S., Mündermann, L., Andriacchi, T., 2007. A framework for the functional identification of joint centers using markerless motion capture, Validation For The Hip Joint. In Journal of Biomechanics
  6. Giese, M. A., Poggio, T., 2003. Neural mechanisms for the recognition of biological movements and action. In Nature Reviews Neuroscience, Vol. 4, pp. 179-192.
  7. Holstein, H., Li, B., 2002. Low Density Feature Point Matching for Articulated Pose Identification. In British Machine Vision Conference 2002, pp 678 - 687
  8. Johansson, G., 1973. Visual perception of biological motion and a model for its analysis. In Perception & Psychophysics, Vol. 14, No. 2, pp. 201 - 211.
  9. Koehler, H., Pruzinec, M., Feldmann, T., Woerner, A., 2008. Automatic Human Model Parametrization From 3D Marker Data For Motion Recognition. In Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision, Pilsen, 2008
  10. Moeslund, T.B., Granum, E., 2001. A survey of computer vision-based human motion capture. In Computer Vision and Image Understanding, Vol. 81, No. 3, pp. 231-268.
  11. Moeslund, T.B., Hilton. A., Krüger, V., 2006. A survey of advances in vision-based human motion capture and analysis. In Computer Vision and Image Understanding, Vol. 104 , No. 2, pp. 90 - 126.
  12. Nicolescu, M., Medioni, G., 2002. Perceptual Grouping from Motion Cues Using Tensor Voting in 4-D. In European Conf. on Computer Vision, LNCS 2352, pp. 423 - 437
  13. Shi, J., Tomasi, C., 1994. Good Features to Track. In IEEE Conf. on Computer Vision and Pattern Recognition, pp. 593 - 600.
  14. Silaghi, M.-C., Plänkers, R., Boulic, R., Fua, P., Thalmann, D., 1998. Local and Global Skeleton Fitting Techniques for Optical Motion Capture. In Proc. of the International Workshop on Modelling and Motion Capture Techniques for Virtual Environments, LNCS 1537, pp. 26-40.
  15. Song, Y., Goncalves, L., Di Bernardo, E., Perona,P., 1999. Monocular Perception of Biological Motion - Detection and Labeling. In Proc. of the Int. Conf. on Computer Vision, Vol. 2, pp. 805-812.
  16. Song, Y., Goncalves, L., Perona, P., 2003. Unsupervised Learning of Human Motion. In IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25 , No. 7, pp. 814-827
  17. Tomasi, C., and Kanade, T., 1991. Detection and tracking of point features. Technical Report, School of Computer Science, Carnegie Mellon University
  18. Ullman, S., 1983. Computational Studies in the Interpretation of Structure and Motion: Summary and Extension. In Human and Machine Vision, Academic Press
Download


Paper Citation


in Harvard Style

Kuehne H. and Woerner A. (2009). MOTION-BASED FEATURE CLUSTERING FOR ARTICULATED BODY TRACKING . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 579-584. DOI: 10.5220/0001786105790584


in Bibtex Style

@conference{visapp09,
author={Hildegard Kuehne and Annika Woerner},
title={MOTION-BASED FEATURE CLUSTERING FOR ARTICULATED BODY TRACKING},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={579-584},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001786105790584},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - MOTION-BASED FEATURE CLUSTERING FOR ARTICULATED BODY TRACKING
SN - 978-989-8111-69-2
AU - Kuehne H.
AU - Woerner A.
PY - 2009
SP - 579
EP - 584
DO - 10.5220/0001786105790584