Abstract
In this paper, we describe the GPU implementation of a markerless full-body articulated human motion tracking system from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation problem solved using particle swarm optimisation (PSO). We model the human body pose with a skeleton-driven subdivision-surface human body model. The optimisation looks for the best match between the silhouettes generated by the projection of the model in a candidate pose and the silhouettes extracted from the original video sequence. In formulating the solution, we exploit the inherent parallel nature of PSO to formulate a GPU-PSO, implemented within the nVIDIATM CUDATM architecture. Results demonstrate that the GPU-PSO implementation recovers the articulated body pose from 10-viewpoint video sequences with significant computational savings when compared to the sequential implementation, thereby increasing the practical potential of our markerless pose estimation approach.
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
Bandouch, J., Engstler, F., Beetz, M.: Evaluation of hierarchical sampling strategies in 3D human pose estimation. In: Proc. British Machine Vision Conference (2008)
Caillette, F., Galata, A., Howard, T.: Real-time 3-D human body tracking using learnt models of behaviour. Computer Vision and Image Understanding 109(2), 112–125 (2008)
Chang, J.F., Chu, S.C., Roddick, J.F., Pan, J.S.: A parallel particle swarm optimization algorithm with communication strategies. J. Inf. Sci. Eng. 21(4), 809–818 (2005)
Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. International Journal of Computer Vision 61(2), 185–205 (2005)
Gall, J., Rosenhan, B., Brox, T., Seidel, H.P.: Optimization and filtering for human motion capture. International Journal of Computer Vision 87(1–2), 75–92 (2010)
Gies, D., Rahmat Samii, Y.: Reconfigurable array design using parallel particle swarm optimization. In: Intl. Symp. Antennas and Propagation Soc., vol. 1, pp. 177–180 (2003)
Ivekovic, S., John, V., Trucco, E.: Markerless multi-view articulated pose estimation using adaptive hierarchical particle swarm optimisation. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 241–250. Springer, Heidelberg (2010)
Ivekovic, S., Trucco, E., Petillot, Y.: Human body pose estimation with particle swarm optimisation. Evolutionary Computation 16(4), 509–528 (2008)
John, V., Trucco, E., Ivekovic, S.: Markerless human articulated tracking using hierarchical particle swarm optimisation. In: Image and Vision Computing (in Press, 2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. conf. on Neural Networks, vol. IV, pp. 1942–1948. IEEE CS Press, Los Alamitos (1995)
Li, J., Wang, X., He, R., Chi, Z.: An efficient fine-grained parallel genetic algorithm based on GPU-accelerated. In: IFIP Int. Conf. on Network and Parallel Computing Workshops, pp. 855–862 (2007)
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., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104(2-3), 90–126 (2006)
Mussi, L., Cagnoni, S.: Particle swarm for pattern matching in image analysis. In: Artificial life and evolutionary computation, pp. 89–98. World Scientific, Singapore (2010)
Mussi, L., Daolio, F., Cagnoni, S.: GPU-based road sign detection using particle swarm optimization. In: IEEE Conf. Intelligent System Design and Applications, pp. 152–157 (2009)
Organic Motion (2010), http://www.organicmotion.com/
Patney, A., Ebeida, M.S., Owens, J.D.: Parallel view-dependent tessellation of Catmull-Clark subdivision surfaces. In: Proc. Conf. on High Performance Graphics, pp. 99–108 (2009)
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization: an overview. Swarm Intelligence 1(1), 33–57 (2007)
Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108(1–2), 4–18 (2007)
Schutte, J.F., Reinbolt, J.A., Fregly, B.J., Haftka, R.T., George, A.D.: Parallel global optimization with the particle swarm algorithm. J. Num. Methods in Eng. 61, 2296–2315 (2003)
Urtasun, R., Fleet, D.J., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: Proceedings of IEEE ICCV, pp. 403–410 (2005)
Veronese, L.d., Krohling, R.A.: Swarm’s flight: accelerating the particles using C-CUDA. In: IEEE Congress on Evolutionary Computation (CEC 2009, pp. 3264–3270 (2009)
Vicon Motion Capture Systems (2010), http://www.vicon.com/
Waintraub, M., Schirru, R., Pereira, C.: Multiprocessor modeling of parallel Particle Swarm Optimization applied to nuclear engineering problems. Progress in Nuclear Energy 51, 680–688 (2009)
Wang, W., Hong, Y., Kou, T.: Performance gains in parallel particle swarm optimization via nVIDIA GPU. In: Workshop on Computational Mathematics and Mechanics 2009 (2009)
Warren, J., Schaefer, S.: A factored approach to subdivision surfaces. Computer Graphics and Applications 24(3), 74–81 (2004)
Xue, S.D., Zeng, J.C.: Parallel asynchronous control strategy for target search with swarm robots. International Journal of Bio-Inspired Computation 1(3), 151–163 (2009)
Zhou, Y., Tan, Y.: GPU-based parallel particle swarm optimization. In: Proc. 2009 IEEE Congress on Evolutionary Computation (CEC 2009), pp. 1493–1500 (2009)
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
Mussi, L., Ivekovic, S., Cagnoni, S. (2010). Markerless Articulated Human Body Tracking from Multi-view Video with GPU-PSO. In: Tempesti, G., Tyrrell, A.M., Miller, J.F. (eds) Evolvable Systems: From Biology to Hardware. ICES 2010. Lecture Notes in Computer Science, vol 6274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15323-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-15323-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15322-8
Online ISBN: 978-3-642-15323-5
eBook Packages: Computer ScienceComputer Science (R0)