A vision-based hybrid particle tracking velocimetry (PTV) technique using a modified cascade correlation peak-finding method

A novel technique for particle tracking velocimetry is presented in this paper to overcome the issue of overlapping particle images encountered in the flows with high particle density or under volumetric illumination conditions. To achieve this goal, algorithms for particle identification and tracking are developed based on current methods and validated with both synthetic and experimental image sets. The results from synthetic image tests show that the particle identification algorithm is able to resolve overlapped particle images up to 50 % under noisy conditions, while keeping the root mean square peak location error under 0.07 pixels. The algorithm is also robust to the size changes up to a size ratio of 5. The tracking method developed from a classic computer vision matching algorithm is capable of capturing a velocity gradient up to 0.3 while maintaining the error under 0.2 pixels. Sensitivity tests were performed to describe the optimum conditions for the technique in terms of particle image density, particle image sizes and velocity gradients, also its sensitivity to errors of the PIV results that guide the tracking process. The comparison with other existing tracking techniques demonstrates that this technique is able to resolve more vectors out of a dense particle image field.