Lagrangian Particle Tracking: a link between localization error and fraction of missed particles


  • Philippe Cornic ONERA, France
  • Frédéric Champagnat ONERA, France
  • Benjamin Leclaire ONERA, France



Double-frame 3D PTV, Lagrangian Particle Tracking, particles localization error


This paper aims at analysing the behaviour of particle localisation error in 3D Lagrangian Particle Tracking (LPT) techniques, with a particular emphasis on general properties, independent of a specific algorithm. Based on the hypothesis that in LPT algorithms, errors on the image formation models are solely due to random noise, we show/prove the existence of a best achievable root mean square error (RMSE) on particle localisation, that, for a setup at a given seeding density, depends only on the noise level. We provide a procedure to estimate this lower bound, and show that it can only be reached if there are no missed detections; further on, we establish a link between localisation error and fraction of missed particles. We illustrate the consistency of this model on the results of the recent First Challenge on LPT (see ISPIV21 papers by Leclaire et al. and Sciacchitano et al.)

Author Biography

Philippe Cornic, ONERA, France

Philippe Cornic (male, 55 years old) graduated from the "Ecole Nationale Supérieure des Ingénieurs
Electriciens de Grenoble" in 1989. He has been working on computer vision at ONERA since 1992 and is
currently a senior research engineer. His main research interests are geometric computer vision and tomographic methods for estimating scalar (BOS) or vector (Particle Image Velocimetry) fields for Fluid Mechanic.






3D Methods and Applications