Event-based imaging velocimetry: an assessment of event-based cameras for the measurement of fluid flows

Abstract

Contrary to conventional frame-based imaging, event-based vision or dynamic vision sensing asynchronously records binary signals of intensity changes for given pixels with microsecond resolution. The present work explores the possibilities of harnessing the potential of event-based vision for fluid flow measurement. The described implementations of event-based imaging velocimetry rely on imaging small particles that are illuminated by a laser light sheet which is similar to classical two-dimensional, two-component (2d–2c) PIV with the difference that a continuously operating laser light sheet is used without modulation of the laser or camera. The moving particles generate continuous time-stamped events on the detector that are later used to infer their velocity using patch-wise processing schemes. Two flow estimation algorithms are proposed; one uses a “motion compensation” that maximizes the local contrast and the other is based on a sum-of-correlations approach. The underlying motion detection schemes in combination with the complete absence of background signal allow straightforward retrieval of the events associated with individual particles thereby allowing the reconstruction of individual particle tracks. Alternatively, the event data can be processed with conventional PIV algorithms using images reconstructed from the event data stream. The concepts are demonstrated on simple flows in water and air. Graphic abstract

Event-based imaging velocimetry using pulsed illumination

“…The pulsed light also makes stationary particles visible which would normally be hidden under constant illumination conditions. In previous implementations of EBIV the lack of events in quiescent regions of a flow have resulted in data loss (Willert and Klinner, 2022a). If timing and pulse energies are carefully adjusted, the pulsed illumination does not result in an avalanche of events that would otherwise saturate the sensor.…”

“…Along with comparative PIV measurements, the statistical and spectral analysis of the processed EBIV data allows an assessment of the overall measurement uncertainty of the proposed technique. The material presented herein extends on a recent publication on the subject (Willert and Klinner, 2022a).…”

Event-Based Background-Oriented Schlieren

“…Some works have explored event cameras for detecting small changes in the scene. These include vibration monitoring [40], particle-image velocimetry [41], and time-resolved 3D fluid flow reconstruction via collimated illumination [42]. These works open another stack of event camera applications in the field of fluid dynamics.…”

A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework

“…[ 3,4 ] The neuromorphic principle of operation of event cameras makes them naturally respond to the scene dynamics. Therefore, they have been used in motion estimation tasks, [ 5,6 ] such as simultaneous localization and mapping, [ 7–12 ] optical flow estimation, [ 13–16 ] particle image velocimetry, [ 17 ] motion segmentation, [ 18,19 ] obstacle avoidance, [ 20 ] and space situational awareness. [ 21 ]…”

“…[3,4] The neuromorphic principle of operation of event cameras makes them naturally respond to the scene dynamics. Therefore, they have been used in motion estimation tasks, [5,6] such as simultaneous localization and mapping, [7][8][9][10][11][12] optical flow estimation, [13][14][15][16] particle image velocimetry, [17] motion segmentation, [18,19] obstacle avoidance, [20] and space situational awareness. [21] Contrast maximization (CMax) [22] is a powerful event processing framework that achieves state-of-the-art accuracy in various motion estimation tasks.…”