A quantitative study of three-dimensional Lagrangian particle tracking algorithms

By Nicholas T. Ouellette, Haitao Xu, Eberhard Bodenschatz. 2006

480 citations recorded by the source corpus.

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.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/dc084b30eab4da371abe5657b39ba7a0aade3c56

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Citations in the corpus

Higher order multi-frame particle tracking velocimetry (2013, methodology): “Oulette et al. (2006) used criteria as the minimal acceleration for the third frame, or minimized change in acceleration for the fourth frame, where a modified version of the latter criteria showed the best results.”
Motion of inertial particles with size larger than Kolmogorov scale in turbulent flows (2008, methodology): “[13] N.”
Shake-The-Box: Lagrangian particle tracking at high particle image densities (2016, background): “At 0.01 ppp, the different algorithms already show between 6 and 25 % of undetected particles (Ouellette et al. 2006), clearly documenting the limits in seeding concentration for this technique.”