Abstract
We present a human body motion tracking system for an interactive virtual simulation training environment. This system captures images using IR illumination and near-IR cameras to overcome limitations of a dimly lit environment. Features, such as silhouettes and medial axis of blobs are extracted from the images which lack much internal texture. We use a combination of a 2D ICP and particle filtering method to estimate the articulated body configuration of a trainee from image features. The method allows articulation of the arms at elbows and shoulders and of the body at the waist; this is a considerable improvement over previous such methods. Our system works in real-time and is robust to temporary errors in image acquisition or tracking. The system serves as part of a multi-modal user-input device for interactive simulation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)
Bernier, O., Chan, P.C.M.: Real-time 3d articulated pose tracking using particle filtering and belief propagation on factor graphs. In: BMVC 2006, vol. 2, p. I:7 (2006)
Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chu, C., Nevatia, R.: Real time body pose tracking in an immersive training environment, pp. 146–156 (2007)
Demirdjian, D., Darrell, T.: 3-d articulated pose tracking for untethered diectic reference. In: Proceedings of ICMI 2002, Washington, DC, USA, p. 267. IEEE Computer Society, Los Alamitos (2002)
Demirdjian, D., Taycher, L., Shakhnarovich, G., Grauman, K., Darrell, T.: Avoiding the “streetlight effect”: tracking by exploring likelihood modes. In: Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, October 17-21, 2005, vol. 1, pp. 357–364 (2005)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: CVPR 2000, pp. II: 126–133 (2000)
Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg
Elgammal, A., Lee, C.-S.: Inferring 3d body pose from silhouettes using activity manifold learning (2004)
Gramkow, C.: On averaging rotations. Int. J. Comput. Vision 42(1-2), 7–16 (2001)
Izo, T., Grimson, W.: Simultaneous pose estimation and camera calibration from multiple views. In: Non-Rigid 2004, p. 14 (2004)
Kehl, R., Bray, M., Van Gool, L.: Full body tracking from multiple views using stochastic sampling. In: IEEE Computer Society Conference on CVPR 2005, vol. 2, pp. 129–136 (2005)
Lee, M., Nevatia, R.: Dynamic human pose estimation using markov chain monte carlo approach. In: Motion 2005, pp. II: 168–175 (2005)
Leymarie, F.F., Kimia, B.B.: Medial Representations: Mathematics, Algorithms and Applications, ch. 11. Kluwer, Dordrecht (to be published, 2006)
Parameswaran, V., Chellappa, R.: View independent human body pose estimation from a single perspective image. In: CVPR 2004, pp. II: 16–22 (2004)
Rahman, M., Ishikawa, S.: Appearance-based representation and recognition of human motions. In: Proceedings. ICRA 2003. IEEE International Conference on Robotics and Automation, 2003, vol. 1, pp. 1410–1415 (2003)
Schmidt, J., Fritsch, J., Kwolek, B.: Kernel particle filter for real-time 3d body tracking in monocular color images. In: FGR 2006, pp. 567–572 (2006)
Wang, P., Rehg, J.: A modular approach to the analysis and evaluation of particle filters for figure tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, June 17-22, 2006, vol. 1, pp. 790–797 (2006)
Zerroug, M., Nevatia, R.: Segmentation and 3-d recovery of curved-axis generalized cylinders from an intensity image. In: Proceedings of the 12th IAPR International Conference on Computer Vision and Image Processing, October 1994, vol. 1, pp. 678–681 (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chu, CW., Nevatia, R. (2008). Real-Time 3D Body Pose Tracking from Multiple 2D Images. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2008. Lecture Notes in Computer Science, vol 5098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70517-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-70517-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70516-1
Online ISBN: 978-3-540-70517-8
eBook Packages: Computer ScienceComputer Science (R0)