Abstract
This paper addresses the problem of probabilistic modelling of human motion by combining several 2D views. This method takes advantage of 3D information avoiding the use of a complex 3D model. Considering that the main disadvantage of 2D models is their restriction to the camera angle, a solution to this limitation is proposed in this paper. A multi-view Gaussian Mixture Model (GMM) is therefore fitted to a feature space made of Shapes and Stick figures manually labelled. Temporal and spatial constraints are considered to build a probabilistic transition matrix. During the fitting, this matrix limits the feature space only to the most probable models from the GMM. Preliminary results have demonstrated the ability of this approach to adequately estimate postures independently of the direction of motion during the sequence.
This work is supported by grant TIC2003-08382-C05-05 from Spanish Ministry of Science and Technology (MCyT) and FEDER.
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References
Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognition 36, 585–601 (2003)
Kakadiaris, I.A., Metaxas, D.N.: Model-based estimation of 3d human motion. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1453–1459 (2000)
Sidenbladh, H., Black, M.J., Sigal, L.: Implicit probabilistic models of human motion for synthesis and tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)
Baumberg, A., Hogg, D.: Learning flexible models from image sequences. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 299–308. Springer, Heidelberg (1994)
Sminchisescu, C., Triggs, B.: Kinematic jump processes for monocular 3d human tracking. In: CVPR (1), pp. 69–76 (2003)
Bowden, R., Mitchell, T.A., Sarhadi, M.: Reconstructing 3d pose and motion from a single camera view. In: BMVC (1998)
Gross, R., Shi, J.: The cmu motion of body (mobo) database, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (2001)
Cootes, T., Taylor, C.: A mixture model for representing shape variation (1997)
Ponsa, D., Roca, F.X.: A novel approach to generate multiple shape models for tracking applications. In: Perales, F.J., Hancock, E.R. (eds.) AMDO 2002. LNCS, vol. 2492, pp. 80–91. Springer, Heidelberg (2002)
Bowden, R., Sarhadi, M.: Building temporal models for gesture recognition. In: BMVC (2000)
Heap, T., Hogg, D.: Wormholes in shape space: Tracking through discontinuous changes in shape. In: ICCV, pp. 344–349 (1998)
Inman, V.T., Ralston, H.J., Todd, F.: Human Walking. Williams and Wilkins, Baltimore (1981)
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Rogez, G., Orrite, C., Martínez, J., Herrero, J.E. (2006). Probabilistic Spatio-temporal 2D-Model for Pedestrian Motion Analysis in Monocular Sequences. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2006. Lecture Notes in Computer Science, vol 4069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11789239_18
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DOI: https://doi.org/10.1007/11789239_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36031-5
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