Toward Event-Based Noise-Robust High Density Particle Velocimetry

The necessity of understanding and analyzing the flow field characteristics has prompted researchers to strive for more precise tools and systems to enhance the accuracy of flow velocity measurements. We propose in this paper the noise-robust Particle Event Velocimetry (PEV), which is a particle-level estimate of the flow field based on the analysis of individual seed particles observed and tracked using event-based camera. Compared to conventional frame-based (FB) Particle Image/Tracking Velocimetry (PIV/PTV) and event-based (EB) particle velocimetry methods, the proposed PEV’s flow field achieves higher spatial resolution limited only by the density of the seeded particles while maintaining experimental simplicity. We also developed an event-based camera simulator for synthetic data with a ground-truth motion field to objectively benchmark the proposed PEV against other FB and EB methods and analyzed these competing methodologies’ ability to reconstruct sharp motion boundaries and flow field curvatures. The results include real-world event-based camera data experiments, in addition to the benchmark results which show that the algorithm developed for PEV was able to achieve a superior velocimetry compared to other algorithms.