Abstract
This paper presents an effective algorithm for 3D model-based human motion tracking using a GPU-accelerated particle swarm optimization. The tracking involves configuring the 3D human model in the pose described by each particle and then rasterizing it in each camera view. In order to accelerate the calculation of the fitness function, which is the most computationally demanding operation of the algorithm, the rendering of the 3D model has been realized using CUDA-OpenGL interoperability. Since CUDA and OpenGL both run on GPU and share data through common memory the CUDA-OpenGL interoperability is very fast. We demonstrate that thanks to GPU hardware rendering the time needed for calculation of the objective function is shorter. Owing to more precise rendering of the 3D model as well as better extraction of its edges the human motion tracing is more accurate.
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.
Similar content being viewed by others
References
Castano-Diez, D., Moser, D., Schoenegger, A., Pruggnaller, S., Frangakis, A.S.: Performance evaluation of image processing algorithms on the GPU. Journal of Structural Biology 164(1), 153–160 (2008)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: IEEE Int. Conf. on Pattern Recognition, pp. 126–133 (2000)
Fung, J., Mann, S.: Using graphics devices in reverse: GPU-based image processing and computer vision. In: IEEE Int. Conf. on Multimedia and Expo., pp. 9–12 (2008)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, pp. 1942–1948. IEEE Press, Piscataway (1995)
Krzeszowski, T., Kwolek, B., Wojciechowski, K.: GPU-accelerated tracking of the motion of 3D articulated figure. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 155–162. Springer, Heidelberg (2010)
Kwolek, B., Krzeszowski, T., Gagalowicz, A., Wojciechowski, K., Josinski, H.: Real-time multi-view human motion tracking using particle swarm optimization with resampling. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds.) AMDO 2012. LNCS, vol. 7378, pp. 92–101. Springer, Heidelberg (2012)
Pulli, K., Baksheev, A., Kornyakov, K., Eruhimov, V.: Real-time computer vision with OpenCV. Comm. ACM 55(6), 61–69 (2012)
Rymut, B., Kwolek, B.: GPU-supported object tracking using adaptive appearance models and Particle Swarm Optimization. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part II. LNCS, vol. 6375, pp. 227–234. Springer, Heidelberg (2010)
Rymut, B., Kwolek, B., Krzeszowski, T.: GPU-accelerated human motion tracking using particle filter combined with PSO. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2013. LNCS, vol. 8192, pp. 426–437. Springer, Heidelberg (2013)
Stam, J.: What every CUDA programmer should know about OpenGL. In: GPU Technology Conference (2009)
Wu, C., Aghajan, H.: Real-time human pose estimation: A case study in algorithm design for smart camera networks. Proc. of the IEEE 96(10), 1715–1732 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Rymut, B., Kwolek, B. (2014). Mixing Graphics and Compute for Real-Time Multiview Human Body Tracking. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_64
Download citation
DOI: https://doi.org/10.1007/978-3-319-11331-9_64
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11330-2
Online ISBN: 978-3-319-11331-9
eBook Packages: Computer ScienceComputer Science (R0)