Comparison Of Particle Matching Algorithms For Ptv

Velocity measurement of fluid flows in an entire area rather than at a single point has been achieved thanks to the fast development in computer science and image processing technique. At present, two image-processing-based velocity measuring methods are widely used, namely, PIV (Particle Image Velocimetry) and PTV (Particle Tracking Velocimetry). Compared with PIV, PTV provides more accurate velocity information, for it directly tracks the motion of tracer particles seeded in the flow and stays free from the averaging effect inherent in PIV. Due to its advantages in measuring certain complex flows, PTV has been experiencing fast development aimed at wider applications.PTV has two major sub-processes, namely, particle identification and particle matching. Algorithm for particles matching, essential for the success of PTV, has been the focus of PTV research. This paper made comparisons of six PTV matching algorithms, including the nearest-neighbor algorithm, the four-frame tracking algorithm, the PCSS algorithm, the SPG algorithm, the matching probability algorithm, and the velocity-gradient tensor algorithm.Comparison of various algorithms was made in terms of calculating efficiency and matching accuracy. A parameter called"time complexity"for the algorithms, defined as the times of basic process being repeated, was proposed to indicate the efficiency.Two other parameters, the matching rateΦ_rand false matching rateΦ_e , were introduced for quantitative comparison of these algorithms in terms of efficiency and accuracy. The comparison was based on three categories of artificial image sequences of various flows, including a horizontal flow, a wavy flow, and a vortex flow, and each category has images of various particle densities. Comparison of the same algorithm was made by using different image series, and then comparison of various algorithms was made by using the same image series.The results show that: (1) the matching-probability algorithm provides the best results in terms of accuracy, (2) the nearest neighbor method is most efficient under low particle density and small flow velocity, (3) the accuracy of the PCSS algorithm is good, but its efficiency is low, and (4) the accuracies of the other three algorithms are all very poor.