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 (EBV) or dynamic vision sensing (DVS) 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 (EBIV) rely on the 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 a patch-wise processing schemes. Two flow estimation algorithms are proposed; one uses a "motion compensation" that maximizes the local contrast, the other is based on a sum-of-correlations approach. The underlying motion detection schemes along with the complete absence of background signal allows 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.