Tracking with the Kinematics of Extremal Contours
This paper addresses the problem of articulated motion tracking from image sequences. We describe a method that relies on an explicit parameterization of the extremal contours in terms of the joint parameters of an associated kinematic model. The latter allows us to predict the extremal contours from the body-part primitives of an articulated model and to compare them with observed image contours. The error function that measures the discrepancy between observed contours and predicted contours is minimized using an analytical expression of the Jacobian that maps joint velocities onto contour velocities. In practice we model people both by their geometry (truncated elliptical cones) and with their articulated structure – a kinematic model with 40 rotational degrees of freedom. We observe image data gathered with several synchronized cameras. The tracker has been successfully applied to image sequences gathered at 30 frames/second.
KeywordsError Function Joint Velocity Image Contour Joint Parameter Image Silhouette
Unable to display preview. Download preview PDF.
- 3.Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. Computer Vision and Pattern Recognition, 2126–2133 (2000)Google Scholar
- 4.Hilton, A.: Towards model-based capture of a persons shape, appearance and motion. In: Proceedings of the IEEE International Workshop on Modelling People (1999)Google Scholar
- 5.Yan, J., Pollefeys, M.: A factorization approach to articulated motion recovery. In: Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 815–821 (2005)Google Scholar
- 6.Drummond, T., Cipolla, R.: Real-time tracking of highly articulated structures in the presence of noisy measurements. In: ICCV, pp. 315–320 (2001)Google Scholar
- 7.Sminchisescu, C., Telea, A.: Human pose estimation from silhouettes. a consistent approach using distance level sets. In: WSCG International Conference on Computer Graphics, Visualization and Computer Vision (2002)Google Scholar
- 9.Niskanen, M., Boyer, E., Horaud, R.: Articulated motion capture from 3-d points and normals. In: Clocksin, F.T. (ed.) British Machine Vision Conference, vol. 1, pp. 439–448. British Machine Vision Association (BMVA), Oxford (2005)Google Scholar
- 10.Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)Google Scholar
- 11.Agarwal, A., Triggs, B.: Learning to track 3d human motion from silhouettes. In: International Conference on Machine Learning, Banff, pp. 9–16 (2004)Google Scholar
- 14.Rosten, E., Drummond, T.: Rapid rendering of apparent contours of implicit surfaces for real-time tracking. In: British Machine Vision Conference, vol. 2, pp. 719–728 (2003)Google Scholar
- 15.McCarthy, J.M.: Introduction to Theoretical Kinematics. MIT Press, Cambridge (1990)Google Scholar