Particle Image Velocimetry by Feature Tracking
Particle Image Velocimetry (PIV) is a popular approach to flow visualisation in hydro- and aerodynamic studies and applications. The fluid is seeded with particles that follow the flow and make it visible. Traditionally, correlation techniques have been used to estimate the displacements of the particles in a digital PIV sequence. In this paper, two efficient feature tracking algorithms are customised and applied to PIV. The algorithmic solutions of the application are described. Techniques for coherence filtering and interpolation of a velocity field are developed. Experimental results are given, demonstrating that the tracking algorithms offer Particle Image Velocimetry a good alternative to the existing techniques.
Keywordsmotion analysis particle image velocimetry feature tracking
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- 3.Birchfield, S.: KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker. http://vision.stanford.edu/birch/klt/
- 4.Chetverikov, D., Nagy, M., Verestoy, J.: Comparison of Tracking Techniques Applied to Digital PIV. Proc. International Conf. on Pattern Recognition 4 (2000) 619–622Google Scholar
- 6.Corpetti, T., Mémin, E., Perez, P.: Estimating Fluid Optical Flow. Proc. International Conf. on Pattern Recognition 3 (2000) 1045–1048Google Scholar
- 8.Jähne, B. Digital Image Processing. Springer (1997)Google Scholar
- 10.Quénot, G.: Data and procedures for development and testing of PIV applications. ftp://ftp.limsi.fr/pub/quenot/opflow/
- 11.Quénot, G.: Performance evaluation of an optical flow technique for particle image velocimetry. Proc. Euromech 406 Colloquim. Warsaw (1999) 177–180Google Scholar
- 12.Standardimages for particle imaging velocimetry. http://www.vsj.or.jp/piv/
- 13.Shi, J., Tomasi, C.: Good features to track. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR94). Seattle (Jun 1994)Google Scholar