Abstract
We present an articulated tracking system working with data from a single narrow baseline stereo camera. The use of stereo data allows for some depth disambiguation, a common issue in articulated tracking, which in turn yields likelihoods that are practically unimodal. While current state-of-the-art trackers utilize particle filters, our unimodal likelihood model allows us to use an unscented Kalman filter. This robust and efficient filter allows us to improve the quality of the tracker while using substantially fewer likelihood evaluations. The system is compared to one based on a particle filter with superior results. Tracking quality is measured by comparing with ground truth data from a marker-based motion capture system.
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References
Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108, 4–18 (2007)
Hauberg, S., Lapuyade, J., Engell-Nørregård, M., Erleben, K., Steenstrup Pedersen, K.: Three dimensional monocular human motion analysis in end-effector space. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 235–248. Springer, Heidelberg (2009)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 126–133. IEEE Comput. Soc, Los Alamitos (2000)
Bandouch, J., Beetz, M.: Tracking humans interacting with the environment using efficient hierarchical sampling and layered observation models. In: IEEE International Workshop on Human-Computer Interaction, vol. 2 (2009)
Kjellström, H., Kragić, D., Black, M.J.: Tracking people interacting with objects. In: CVPR 2010: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010)
Hauberg, S., Sommer, S., Pedersen, K.S.: Gaussian-like spatial priors for articulated tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 425–437. Springer, Heidelberg (2010)
Julier, S., Uhlmann, J.: A new extension of the Kalman filter to nonlinear systems. In: Int. Symp. Aerospace/Defense Sensing, Simul. and Controls, vol. 3, p. 26 (1997)
Wan, E., Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: Proceedings of Symposium, pp. 153–158 (2000)
Lu, Z., Carreira-Perpinan, M., Sminchisescu, C.: People Tracking with the Laplacian Eigenmaps Latent Variable Model. In Platt, J., Koller, D., Singer, Y., Roweis, S., eds.: Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, MA (2008) 1705–1712
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)
Elgammal, A.M., Lee, C.S.: Tracking People on a Torus. IEEE Transaction on Pattern Analysis and Machine Intelligence 31, 520–538 (2009)
Urtasun, R., Fleet, D.J., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 403–410 (2005)
Loy, G., Eriksson, M., Sullivan, J., Carlsson, S.: Monocular 3D reconstruction of human motion in long action sequences. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 442–455. Springer, Heidelberg (2004)
Sminchisescu, C., Triggs, B.: Kinematic Jump Processes for Monocular 3D Human Tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 69–76 (2003)
Ziegler, J., Nickel, K., Stiefelhagen, R.: Tracking of the articulated upper body on multi-view stereo image sequences. In: CVPR 2006: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 774–781. IEEE Computer Society, Washington, DC, USA (2006)
Stenger, B., Mendonca, P.R.S., Cipolla, R.: Model-based hand tracking using an unscented kalman filter. In: Proc. British Machine Vision Conference, vol. I, pp. 63–72 (2001)
Cappé, O., Godsill, S., Moulines, E.: An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE 95, 899–924 (2007)
Balan, A.O., Sigal, L., Black, M.J.: A Quantitative Evaluation of Video-based 3D Person Tracking. In: Proceedings of the 14th International Conference on Computer Communications and Networks, pp. 349-356. IEEE Computer Society, Los Alamitos (2005)
Bandouch, J., Engstler, F., Beetz, M.: Accurate human motion capture using an ergonomics-based anthropometric human model. In: Proc. of the 5th Int. Conf. on Articulated Motion and Deformable Objects, pp. 248–258. Springer, Heidelberg (2008)
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Larsen, A.B.L., Hauberg, S., Pedersen, K.S. (2011). Unscented Kalman Filtering for Articulated Human Tracking. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_22
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DOI: https://doi.org/10.1007/978-3-642-21227-7_22
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