Abstract
This paper presents our approach to 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 particle’s 2D plane. In our implementation, we launch one independent thread for each column of each 2D plane. Such a parallel algorithm exhibits the level of parallelism that allows us to effectively utilize the GPU resources. Owing to such task decomposition the tracking of the full human body can be performed at rates of 15 frames per second. The GPU achieves an average speedup of 7.5 over the CPU. The speedup that achieves the GPU over CPU grows with the number of the particles. For marker-less motion capture system consisting of four calibrated and synchronized cameras, the efficiency comparisons were conducted on four CPU cores and four GTX GPUs on two cards.
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Box, G.E.P., Muller, M.E.: A note on the generation of random normal deviates. Ann. Math. Stat. 29(2), 610–611 (1958)
Brown, J., Capson, D.: Framework for 3d model-based visual tracking using a GPU-accelerated particle filter. IEEE Trans. Vis. Comput. Graph. 18(1), 68–80 (2012)
Castano-Diez, D., Moser, D., Schoenegger, A., Pruggnaller, S., Frangakis, A.S.: Performance evaluation of image processing algorithms on the GPU. J. Struct. Biol. 164(1), 153–160 (2008)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: IEEE International Conference on Pattern Recognition, pp. 126–133 (2000)
Hansen, N., Auger, A., Ros, R., Finck, S., Pošík, P.: Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In: Genetic and Evolutionary Computation Conference. GECCO’10, pp. 1689–1696. ACM (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press, Piscataway, NJ (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)
Krzeszowski, T., Michalczuk, A., Kwolek, B., Switonski, A., Josinski, H.: Gait recognition based on marker-less 3D motion capture. In: 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 232–237 (2013)
Kwolek, B., Krzeszowski, T., Wojciechowski, K.: Swarm intelligence based searching schemes for articulated 3d body motion tracking. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 115–126. Springer, Heidelberg (2011)
Laguna-Sanchez, G.A., Olguin-Carbajal, M., Cruz-Cortes, N., Barron-Fernandez, R., Alvarez-Cedillo, J.A.: Comparative study of parallel variants for a particle swarm optimization. J. Appl. Res. Technol. 7(3), 292–309 (2009)
Lee, V.W., Kim, C., Chhugani, J., Deisher, M., Kim, D., Nguyen, A.D., Satish, N., Smelyanskiy, M., Chennupaty, S., Hammarlund, P., Singhal, R., Dubey, P.: Debunking the 100x GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU. In: Proceedings of the 37th Annual International Symposium on Computer Architecture. ISCA’10, pp. 451–460. ACM, New York, NY, USA (2010)
Mussi, L., Ivekovic, S., Cagnoni, S.: Markerless articulated human body tracking from multi-view video with GPU-PSO. In: Tempesti, G., Tyrrell, A.M., Miller, J.F. (eds.) ICES 2010. LNCS, vol. 6274, pp. 97–108. Springer, Heidelberg (2010)
Pulli, K., Baksheev, A., Kornyakov, K., Eruhimov, V.: Real-time computer vision with OpenCV. Commun. ACM 55(6), 61–69 (2012)
Solomon, S., Thulasiraman, P., Thulasiram, R.: Collaborative multi-swarm PSO for task matching using graphics processing units. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1563–1570 (2011)
Wu, C., Aghajan, H.: Human pose estimation in vision networks via distributed local processing and nonparametric belief propagation. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 1006–1017. Springer, Heidelberg (2008)
Zhou, Y., Tan, Y.: GPU-based parallel particle swarm optimization. In: IEEE Congress on Evolutionary Computation. CEC’09, pp. 1493–1500 (2009)
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This work has been supported by the National Science Center (NCN) within the research project N N516 483240.
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Rymut, B., Kwolek, B. (2014). Real-Time Multiview Human Body Tracking Using GPU-Accelerated PSO. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55224-3_43
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DOI: https://doi.org/10.1007/978-3-642-55224-3_43
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