Abstract
This paper presents the parallelization of the particle filter algorithm in a single target video tracking application. In this document we demonstrate the process by which we parallelized the particle filter algorithm, beginning with a MATLAB implementation. The final CUDA program provided approximately 71x speedup over the initial MATLAB implementation.
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
Aksel, A., Scott, T.A.: Target Tracking Using Snake Particle Filter. In: 2010 Southwest Symposium on Image Analysis and Interpretation. IEEE Computer Society, Austin (2010)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)
Box, G.E.P., Muller, M.E.: A Note on the Generation of Random Normal Deviates. The Annals of Mathematical Statistics 29(2), 610–611 (1958)
Boyer, M., Tarjan, D., Acton, S., Skadron, K.: Accelerating Leukocyte Tracking using CUDA: A Case Study in Leveraging Manycore Coprecessors. In: 23rd IEEE International Parallel and Distributed Processing Symposium. IEEE, Rome (2009)
Ferreira, Filipe, J., Lobo, J., Dias, J.: Bayesian real-time perception algorithms on GPU. Journal of Real-Time Image Processing, Special Issue (2010)
Gilliam, A.D., Epstein, F.H., Acton, S.T.: Cardiac Motion Recovery via Active Trajectory Field Models. IEEE Transactions in Biomedicine 13(2) (2009)
Lenz, C., Panin, G., Knoll, A.: A GPU-Accelerated Particle Filter with Pixel-Level Likelihood. In: International Workshop on Vision Modeling and Virtualization, Konstanz, Germany (2008)
Lozano, O.M., Otsuka, K.: Real-time visual tracker by stream processing. Journal of Signal Processing Systems 57(2), 285–295 (2009)
Nummiaro, K., Koller-Meier, E., Van Gool, L.: An Adaptive Color-based Particle Filter. Image and Vision Computing 21(1), 99–110 (2003)
nVidia.: CUDA Reference Manual 2.3. CUDA ZONE (July 1, 2009). http://developer.download.nvidia.com/compute/cuda/2_3/toolkit/docs/CUDA_Reference_Manual_2.3.pdf (accessed, October 24, 2009).
Quinn, M.J.: Parallel Programming in C with MBI and OpenMP. McGraw-Hill, New York (2004)
Szafaryn, L.G., Skadron, K., Saucerman, J.J.: Experiences Accelerating MATLAB Systems Biology Applications. In: Proceedings of the Workshop on Biomedicine in Computing: Systems, Architectures, and Circuits, BiC (2009)
Thrust.: Thrust: C++ Template Library for CUDA. http://code.google.com/p/thrust/ (accessed April 23, 2010).
Ulman, G..: Bayesian Particle Filter Tracking with CUDA. (April 2010), http://csi702.net/csi702/images/Ulman_report_final.pdf (accessed May 14, 2010).
Eide, V.S.W., Eliassen, F., Granmo, O.-C., Lysne, O.: Scalable Independent Multi-level Distribution in Multimedia Content Analysis. In: Boavida, F., Monteiro, E., Orvalho, J. (eds.) IDMS 2002 and PROMS 2002. LNCS, vol. 2515, pp. 37–48. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goodrum, M.A., Trotter, M.J., Aksel, A., Acton, S.T., Skadron, K. (2011). Parallelization of Particle Filter Algorithms. In: Varbanescu, A.L., Molnos, A., van Nieuwpoort, R. (eds) Computer Architecture. ISCA 2010. Lecture Notes in Computer Science, vol 6161. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24322-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-24322-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24321-9
Online ISBN: 978-3-642-24322-6
eBook Packages: Computer ScienceComputer Science (R0)