Abstract
One of the most used techniques for full-body human tracking consists of estimating the probability of the parameters of a human body model over time by means of a particle filter. However, given the high-dimensionality of the models to be tracked, the number of required particles to properly populate the space of solutions makes the problem computationally very expensive. To overcome this, we present an efficient scheme which makes use of an action-specific model of human postures to guide the prediction step of the particle filter, so only feasible human postures are considered. As a result, the prediction step of this model-based tracking approach samples from a first order motion model only those postures which are accepted by our action-specific model. In this manner, particles are propagated to locations in the search space with most a posteriori information avoiding particle wastage. We show that this scheme improves the efficiency and accuracy of the overall tracking approach.
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References
Chai, J., Hodgins, J.K.: Performance animation from low-dimensional control signals. SIGGRAPH 2005, ACM Trans. Graph. 24(3), 686–696 (2005)
Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. IJCV 61(2), 185–205 (2005)
Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
Gonzàlez, J.: Human Sequence Evaluation: the Key-frame Approach. PhD thesis, Universitat Autònoma de Barcelona (2004)
González, J., Varona, J., Roca, F.X., Villanueva, J.J.: Analysis of human walking based on aSpaces. In: Perales, F.J., Draper, B.A. (eds.) AMDO 2004. LNCS, vol. 3179, pp. 177–188. Springer, Heidelberg (2004)
Horprasert, T., Harwood, D., Davis, L.S.: A robust background subtraction and shadow detection. In: Proc. Asian. Conf. on Comp. Vision (January 2000)
MacCormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface-quality hand tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)
Ning, H., Tan, T., Wang, L., Hu, W.: Kinematics-based tracking of human walking in monocular video sequences. IVC 22, 429–441 (2004)
Rius, I., Rowe, D., González, J., Roca, F.X.: A 3D dynamic model of human actions for probabilistic image tracking. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 529–536. Springer, Heidelberg (2005)
Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic tracking of 3d human figures using 2d image motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (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)
Stadler, W.: Analytical Robotics and Mechatronics. McGraw-Hill, NY, USA (1995)
Urtasun, R., Fleet, D.J., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: IEEE International Conference on Computer Vision (ICCV 2005), vol. 1, pp. 403–410 (2005)
Wachter, S., Nagel, H.H.: Tracking persons in monocular image sequences. CVIU 74(3), 174–192 (1999)
Wu, Y., Lin, J., Huang, T.S.: Analyzing and capturing articulated hand motion in image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(12), 1910–1922 (2005)
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Rius, I., Varona, J., Roca, X., González, J. (2006). Posture Constraints for Bayesian Human Motion Tracking. 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_43
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DOI: https://doi.org/10.1007/11789239_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36031-5
Online ISBN: 978-3-540-36032-2
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