Digital Particle Image Velocimetry (DPIV) aims at flow visualisation and measurement of flow dynamics in numerous applications, including hydrodynamics, combustion processes and aeronautical phenomena. The fluid is seeded with particles that follow the flow and efficiently scatter light. Traditionally, FFT-based correlation techniques have been used to estimate the displacements of the particles in a digital PIV sequence. Recently, an optical flow estimation technique  developed in computer vision has been successfully applied to DPIV. In this paper we study the DPIV-efficiency of another group of tracking approaches, the feature tracking techniques. Velocity fields obtained by several methods are compared for synthetic and real PIV sequences. It is concluded that feature tracking algorithms applied to DPIV are a useful alternative to both the correlation and the optical flow algorithms.
|Number of pages||4|
|Journal||Proceedings - International Conference on Pattern Recognition|
|Publication status||Published - Dec 1 2000|
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition