Abstract
A neural network particle finding algorithm and a new four-frame predictive tracking algorithm are proposed for three-dimensional Lagrangian particle tracking (LPT). A quantitative comparison of these and other algorithms commonly used in three-dimensional LPT is presented. Weighted averaging, one-dimensional and two-dimensional Gaussian fitting, and the neural network scheme are considered for determining particle centers in digital camera images. When the signal to noise ratio is high, the one-dimensional Gaussian estimation scheme is shown to achieve a good combination of accuracy and efficiency, while the neural network approach provides greater accuracy when the images are noisy. The effect of camera placement on both the yield and accuracy of three-dimensional particle positions is investigated, and it is shown that at least one camera must be positioned at a large angle with respect to the other cameras to minimize errors. Finally, the problem of tracking particles in time is studied. The nearest neighbor algorithm is compared with a three-frame predictive algorithm and two four-frame algorithms. These four algorithms are applied to particle tracks generated by direct numerical simulation both with and without a method to resolve tracking conflicts. The new four-frame predictive algorithm with no conflict resolution is shown to give the best performance. Finally, the best algorithms are verified to work in a real experimental environment.
Similar content being viewed by others
References
Adrian RJ (1991) Particle-imaging techniques for experimental fluid mechanics. Annu Rev Fluid Mech 23:261–304
Bourgeois F, Lassalle J-C (1971) An extension of the Munkres algorithm for the assignment problem to rectangular matrices. Commun ACM 14:802–804
Carosone F, Cenedese A, Querzoli G (1995) Recognition of partially overlapped particle images using the Kohonen neural network. Exp Fluids 19:225–232
Chen Y, Chwang AT (2003) Particle image velocimetry system with self-organized feature map algorithm. J Eng Mech ASCE 129:1156–1163
Chetverikov D, Verestóy J (1999) Feature point tracking for incomplete trajectories. Computing 62:321–338
Cowen EA, Monismith SG (1997) A hybrid digital particle tracking velocimetry technique. Exp Fluids 22:199–211
Doh D-H, Kim D-H, Choi S-H, Hong S-D, Saga T, Kobayashi T (2000) Single-frame (two-field image) 3-D PTV for high speed flows. Exp Fluids 29:S85–S98
Dracos Th (1996) Particle tracking in three-dimensional space. In: Dracos Th (ed) Three-dimensional velocity and vorticity measuring and image analysis techniques. Kluwer, Dordrecht
Grant I, Pan X (1997) The use of neural techniques in PIV and PTV. Meas Sci Technol 8:1399–1405
Grant I, Pan X, Romano F, Wang X (1998) Neural-network method applied to the stereo image correspondence problem in three-component particle image velocimetry. Appl Opt 37:3656–3663
Guezennec YG, Brodkey RS, Trigui N, Kent JC (1994) Algorithms for fully automated three-dimensional particle tracking velocimetry. Exp Fluids 17:209–219
La Porta A, Voth GA, Crawford AM, Alexander J, Bodenschatz E (2001) Fluid particle accelerations in fully developed turbulence. Nature 409:1017–1019
Labonté G (1999) A new neural network for particle-tracking velocimetry. Exp Fluids 26:340–346
Labonté G (2001) Neural network reconstruction of fluid flows from tracer-particle displacements. Exp Fluids 30:399–409
Maas H-G (1996) Contributions of digital photogrammetry to 3-D PT. In: Dracos Th (ed) Three-dimensional velocity and vorticity measuring and image analysis techniques. Kluwer, Dordrecht
Maas H-G, Gruen A, Papantoniou D (1993) Particle tracking velocimetry in three-dimensional flows—part 1. Photogrammetric determination of particle coordinates. Exp Fluids 15:133–146
Malik NA, Dracos Th, Papantoniou DA (1993) Particle tracking velocimetry in three-dimensional flows—part 2. Particle tracking. Exp Fluids 15:279–294
Mann J, Ott S, Andersen JS (1999) Experimental study of relative, turbulent diffusion. Risø National Laboratory Report Risø-R-1036(EN)
Mitchell TM (1997) Machine learning. McGraw-Hill, Boston, pp 81–127
Mordant N, Crawford AM, Bodenschatz E (2004) Experimental Lagrangian acceleration probability density function measurement. Physica D 193:245–251
Ohmi K, Li H-Y (2000) Particle-tracking velocimetry with new algorithms. Meas Sci Technol 11:603–616
Sethi IK, Jain R (1987) Finding trajectories of feature points in a monocular image sequence. IEEE Trans Pattern Anal Mach Intell 9:56–73
Veenman CJ, Reinders MJT, Backer E (2001) Resolving motion correspondence for densely moving points. IEEE Trans Pattern Anal Mach Intell 23:54–72
Veenman CJ, Reinders MJT, Backer E (2003) Establishing motion correspondence using extended temporal scope. Artif Intell 145:227–243
Voth GA, La Porta A, Crawford AM, Alexander J, Bodenschatz E (2002) Measurement of particle accelerations in fully developed turbulence. J Fluid Mech 469:121–160
Westerweel J (1993) Digital particle image velocimetry—theory and applications. PhD Dissertation, Delft University Press
Westerweel J (2000) Theoretical analysis of the measurement precision in particle image velocimetry. Exp Fluids 29:S3–S12
Yeung PK (2002) Lagrangian investigations of turbulence. Annu Rev Fluid Mech 34:115–142
Acknowledgements
The authors would like to thank Lance Collins at Cornell University for contributing DNS data for testing the tracking algorithms presented here. This work was supported by the National Science Foundation under grants PHY-9988755 and PHY-0216406.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ouellette, N.T., Xu, H. & Bodenschatz, E. A quantitative study of three-dimensional Lagrangian particle tracking algorithms. Exp Fluids 40, 301–313 (2006). https://doi.org/10.1007/s00348-005-0068-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00348-005-0068-7