Abstract
This paper presents a novel tracking algorithm, the dynamic kernel-based progressive particle filter (DKPPF), for markless 3D human body tracking. An articulated human body contains considerable degrees of freedom to be estimated. The proposed algorithm aims to reduce the computational complexity and improve the accuracy. The DKPPF decomposes the high dimensional parameter space into three low dimensional spaces and hierarchically searches the posture coefficients. Moreover, it applies multiple predictions and a mean shift tracker to estimate the human posture iteratively. A dynamic kernel model is proposed to automatically adjust the kernel bandwidth of mean shift trackers according to the probability distribution of the posture states. The kernel model is capable of improving the accuracy of the tracking result. The experimental examples show that the proposed approach can effectively improve the accuracy and expedite the computation.
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
Isard, M., Blake, A.: CONDENSATION-Conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–98 (1998)
Deutscher, J., Blake, A., Reid, L.: Articulated Body Motion Capture by Annealed Particle Filtering. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 126–133 (2000)
Lin, S.-Y., Chang, I.-C.: 3D Human Motion Tracking Using Progressive Particle Filter. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 833–842. Springer, Heidelberg (2008)
Agarwal, A., Triggs, B.: Recovering 3D human pose from monocular images. IEEE Transaction on Patten Analysis and Machine Intelligence 28(1), 44–45 (2006)
Agarwal, A., Triggs, B.: Learning to track 3D human motion from silhouettes. In: ACM International Conference Proceeding Series Proceedings of the twenty-first international conference on Machine learning, vol. 69 (2004)
Urtasun, R., Fleet, D.J., Fua, P.: 3D People Tracking with Gaussian Process Dynamic Models. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 238–245 (2006)
Navarantnam, R., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Hierarchical Part-Based Human Body Pose Estimation. In: Proc. British Machine Vision Conf., vol. 1, pp. 479–488 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, SY., Chang, IC. (2010). Dynamic Kernel-Based Progressive Particle Filter for 3D Human Motion Tracking. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-12304-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12303-0
Online ISBN: 978-3-642-12304-7
eBook Packages: Computer ScienceComputer Science (R0)