Experiments in Fluids

, 58:165 | Cite as

Three-dimensional particle tracking velocimetry using dynamic vision sensors

  • D. Borer
  • T. Delbruck
  • T. Rösgen


A fast-flow visualization method is presented based on tracking neutrally buoyant soap bubbles with a set of neuromorphic cameras. The “dynamic vision sensors” register only the changes in brightness with very low latency, capturing fast processes at a low data rate. The data consist of a stream of asynchronous events, each encoding the corresponding pixel position, the time instant of the event and the sign of the change in logarithmic intensity. The work uses three such synchronized cameras to perform 3D particle tracking in a medium sized wind tunnel. The data analysis relies on Kalman filters to associate the asynchronous events with individual tracers and to reconstruct the three-dimensional path and velocity based on calibrated sensor information.


  1. Bar-Shalom Y, Fortmann TE (1988) Tracking and data association. Mathematics in science and engineering, vol 179. Academic Press, CambridgeMATHGoogle Scholar
  2. Drazen D, Lichtsteiner P, Häfliger P, Delbrück T, Jensen A (2011) Toward real-time particle tracking using an event-based dynamic vision sensor. Exp Fluids 51(5):1465CrossRefGoogle Scholar
  3. Elsinga GE, Scarano F, Wieneke B, van Oudheusden BW (2006) Tomographic particle image velocimetry. Exp Fluids 41:933–947CrossRefGoogle Scholar
  4. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press, CambridgeMATHGoogle Scholar
  5. Lichtsteiner P, Posch C, Delbruck T (2008) A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor. IEEE J Solid-State Circuits 43(2):566–576CrossRefGoogle Scholar
  6. Posch C, Serrano-Gotarredona T, Linares-Barranco B, Delbruck T (2014) Retinomorphic event-based vision sensors: bioinspired cameras with spiking output. Proc IEEE 102(10):1470–1484CrossRefGoogle Scholar
  7. Reid D (1979) An algorithm for tracking multiple targets. IEEE Trans Autom Control 24(6):843–854CrossRefGoogle Scholar
  8. Schanz D, Schröder A, Gesemann S (2016) Shake-The-Box: lagrangian particle tracking at high particle image densities. Exp Fluids 57:70CrossRefGoogle Scholar
  9. Tsai RY (1986) An efficient and accurate camera calibration technique for recognition. In: Proceedings of IEEE CV PR’86. pp 364–374Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Institute of Fluid Dynamics, ETH ZurichZurichSwitzerland
  2. 2.Institute of Neuroinformatics, University of ZurichZurichSwitzerland
  3. 3.ETH ZurichZurichSwitzerland

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