Abstract
In this paper we present our work on markerless model-based 3D human motion capture using multiple cameras. We use an industry proven anthropometric human model that was modeled taking ergonomic considerations into account. The outer surface consists of a precise yet compact 3D surface mesh that is mostly rigid on body part level apart from some small but important torsion deformations. Benefits are the ability to capture a great amount of possible human appearances with high accuracy while still having a simple to use and computationally efficient model. We have introduced special optimizations such as caching into the model to improve its performance in tracking applications. Available force and comfort measures within the model provide further opportunities for future research.
3D articulated pose estimation is performed in a Bayesian framework, using a set of hierarchically coupled local particle filters for tracking. This makes it possible to sample efficiently from the high dimensional space of articulated human poses without constraining the allowed movements. Sequences of tracked upper-body as well as full-body motions captured by three cameras show promising results. Despite the high dimensionality of our model (51 DOF) we succeed at tracking using only silhouette overlap as weighting function due to the precise outer appearance of our model and the hierarchical decomposition.
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References
Anguelov, D., Koller, D., Pang, H.-C., Srinivasan, P., Thrun, S.: Recovering articulated object models from 3d range data. In: AUAI 2004: Proceedings of the 20th conference on Uncertainty in artificial intelligence, Arlington, Virginia, United States, pp. 18–26. AUAI Press (2004)
Balan, A.O., Sigal, L., Black, M.J.: A quantitative evaluation of video-based 3d person tracking. In: ICCCN 2005: Proceedings of the 14th International Conference on Computer Communications and Networks, Washington, DC, USA, pp. 349–356. IEEE Computer Society, Los Alamitos (2005)
Bray, M., Koller-Meier, E., Gool, L.V.: Smart particle filtering for high-dimensional tracking. Computer Vision and Image Understanding (CVIU) 106(1), 116–129 (2007)
Bubb, H., Engstler, F., Fritzsche, F., Mergl, C., Sabbah, O., Schaefer, P., Zacher, I.: The development of RAMSIS in past and future as an example for the cooperation between industry and university. International Journal of Human Factors Modelling and Simulation 1(1), 140–157 (2006)
Cheung, K.M., Baker, S., Kanade, T.: Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (June 2003)
Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. International Journal of Computer Vision (IJCV) 61(2), 185–205 (2005)
Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing 10(3), 197–208 (2000)
Grauman, K., Shakhnarovich, G., Darrell, T.: Inferring 3d structure with a statistical image-based shape model. In: ICCV 2003: Proceedings of the Ninth IEEE International Conference on Computer Vision, Washington, DC, USA, p. 641. IEEE Computer Society, Los Alamitos (2003)
Kehl, R., Gool, L.V.: Markerless tracking of complex human motions from multiple views. Computer Vision and Image Understanding (CVIU) 104(2), 190–209 (2006)
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)
Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Computer Vision and Image Understanding (CVIU) 81(3), 231–268 (2001)
Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding (CVIU) 104(2), 90–126 (2006)
Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding (CVIU) 108(1-2), 4–18 (2007)
Rosenhahn, B., Brox, T., Kersting, U., Smith, A., Gurney, J., Klette, R.: A system for marker-less motion capture. Künstliche Intelligenz 20(1), 45–51 (2006)
Seitz, T., Recluta, D., Zimmermann, D.: An approach for a human posture prediction model using internal/external forces and discomfort. In: Proceedings of the SAE 2005 World Congress (2005)
Taylor, G.W., Hinton, G.E., Roweis, S.T.: Modeling human motion using binary latent variables. In: Proc. of the 20th Annual Conference on Neural Information Processing Systems (NIPS), pp. 1345–1352. MIT Press, Cambridge (2006)
Urtasun, R., Fleet, D., Fua, P.: 3D People Tracking with Gaussian Process Dynamical Models. In: Conference on Computer Vision and Pattern Recognition, pp. 238–245 (2006)
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Bandouch, J., Engstler, F., Beetz, M. (2008). Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model. 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_24
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DOI: https://doi.org/10.1007/978-3-540-70517-8_24
Publisher Name: Springer, Berlin, Heidelberg
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