A quantitative study of track initialization of the four-frame best estimate algorithm for three-dimensional Lagrangian particle tracking

Abstract

We introduce a method to improve three-dimensional particle tracking velocimetry (3D-PTV) algorithms. 3D-PTV is an experimental method used to measure the Lagrangian trajectories of individual particles over time. The trajectories are constructed by linking the particle positions from a sequence of images. Different 3D-PTV algorithms have been proposed in the literature, ranging from simply taking the nearest neighbor in the next frame to using multiframe schemes. This work focuses on the initialization of the four-frame best estimate (4BE) method introduced by Ouellette et al (2006 Exp. Fluids 40 301–13). Previously, tracking algorithms have been initialized by using the particle’s nearest neighbor(s) in the next frame or by using a velocity guess to predict the particle’s location in the next frame. We propose a more robust initialization, coupled with 4BE, that performs better than existing methods in the literature, in the sense of yielding a higher number of correct tracks. The performance of the proposed initialization method is compared to the 4BE method that uses nearest neighbor initialization by applying both methods on direct numerical simulation data from the Johns Hopkins turbulence databases. We show that the modified initialization greatly improves tracking in two canonical cases, homogeneous isotropic turbulence and turbulent channel flow (inhomogenous and anisotropic), greatly increasing the percentage of correct tracks found even under challenging seeding/particle displacement conditions.