Abstract
A probabilistic method for tracking 3D articulated human figures in monocular image sequences is presented. Within a Bayesian framework, we define a generative model of image appearance, a robust likelihood function based on image graylevel differences, and a prior probability distribution over pose and joint angles that models how humans move. The posterior probability distribution over model parameters is represented using a discrete set of samples and is propagated over time using particle filtering. The approach extends previous work on parameterized optical flow estimation to exploit a complex 3D articulated motion model. It also extends previous work on human motion tracking by including a perspective camera model, by modeling limb self occlusion, and by recovering 3D motion from a monocular sequence. The explicit posterior probability distribution represents ambiguities due to image matching, model singularities, and perspective projection. The method relies only on a frame-to-frame assumption of brightness constancy and hence is able to track people under changing viewpoints, in grayscale image sequences, and with complex unknown backgrounds.
Chapter PDF
Keywords
- Joint Angle
- Image Motion
- Perspective Projection
- Posterior Probability Distribution
- Prior Probability Distribution
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
M. J. Black. Explaining optical flow events with parameterized spatio-temporal models. CVPR, pp. 326–332, 1999.
M. J. Black and D. J. Fleet. Probabilistic detection and tracking of motion discontinuities. ICCV, pp. 551–558, 1999.
A. Bobick and J. Davis. An appearance-based representation of action. ICPR, 1996.
M. Brand. Shadow puppetry. ICCV, pp. 1237–1244, 1999.
C. Bregler and J. Malik. Tracking people with twists and exponential maps. CVPR, 1998.
T-J. Cham and J. M. Rehg. A multiple hypothesis approach to figure tracking. CVPR, pp. 239–245, 1999.
J. Deutscher, B. North, B. Bascle, and A. Blake. Tracking through singularities and discontinuities by random sampling. ICCV, pp. 1144–1149, 1999.
D. M. Gavrila. The visual analysis of human movement: a survey. CVIU, 73(1):82–98, 1999.
D. M. Gavrila and L. S. Davis. 3-D model-based tracking of humans in action: A multi-view approach. CVPR, pp. 73–80, 1996.
L. Goncalves, E. Di Bernardi, E. Ursella, and P. Perona. Monocular tracking of the human arm in 3D. ICCV, 1995.
N. Gordon, D. J. Salmond, and A. F. M. Smith. A novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. Radar, Sonar and Navigation, 140(2):107–113, 1996.
D. Hogg. Model-based vision: A program to see a walking person. Image and Vision Computing, 1(1):5–20, 1983.
M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. ECCV, pp. 343–356, 1996.
S. X. Ju, M. J. Black, and Y. Yacoob. Cardboard people: A parameterized model of articulated motion. Int. Conf. on Automatic Face and Gesture Recognition, pp. 38–44, 1996.
I. Kakadiaris and D. Metaxas. Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection. CVPR, pp. 81–87, 1996.
M. E. Leventon and W. T. Freeman. Bayesian estimation of 3-d human motion from an image sequence. TR-98-06, Mitsubishi Electric Research Lab, 1998.
D. Morris and J. M. Rehg. Singularity analysis for articulated object tracking. CVPR, pp. 289–296, 1998.
V. Pavolvić, J. Rehg, T-J. Cham, and K. Murphy. A dynamic Bayesian network approach to figure tracking using learned dynamic models. ICCV, pp. 94–101, 1999.
J. O. Ramsay and B. W. Silverman. Functional data analysis. New York: Springer Verlag, 1997.
H. Sidenbladh, F. de la Torre, and M. J. Black. A framework for modeling the appearance of 3D articulated figures. Int. Conf. on Automatic Face and Gesture Recognition, 2000.
S. Wachter and H. H. Nagel. Tracking persons in monocular image sequences. CVIU, 74(3):174–192, 1999.
C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland. Pfinder: Real-time tracking of the human body. PAMI, 19(7):780–785, 1997.
Y. Yacoob and M. J. Black. Parameterized modeling and recognition of activities in temporal surfaces. CVIU, 73(2):232–247, 1999.
Y. Yacoob and L. Davis. Learned temporal models of image motion. ICCV, pp. 446–453, 1998.
M. Yamamoto, A. Sato, S. Kawada, T. Kondo, and Y. Osaki. Incremental tracking of human actions from multiple views. CVPR, pp. 2–7, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sidenbladh, H., Black, M.J., Fleet, D.J. (2000). Stochastic Tracking of 3D Human Figures Using 2D Image Motion. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45053-X_45
Download citation
DOI: https://doi.org/10.1007/3-540-45053-X_45
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67686-7
Online ISBN: 978-3-540-45053-5
eBook Packages: Springer Book Archive