Application of feature matching trajectory detection algorithm for particle streak velocimetry
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R E G UL A R P A P E R
Yusaku Tsukamoto • Shumpei Funatani
Application of feature matching trajectory detection algorithm for particle streak velocimetry
Received: 6 May 2020 / Revised: 8 June 2020 / Accepted: 9 June 2020 Ó The Visualization Society of Japan 2020
Abstract We detect the trajectory of particles using the feature matching method to improve the resolution of particle streak velocimetry (PSV), which is used to measure the velocity of particles from a visualized path line. PSV has a more reliable performance in particle matching as compared to particle tracking velocimetry and is therefore less likely to cause erroneous matching even in high-density images. The center of gravity of the first and last trajectories is obtained to calculate the displacement. The trajectory of the particle is illuminated using a diode laser and imaged using a digital single-lens reflex camera; the trajectory is then divided into three parts and recorded in a single frame using coded illumination. The first and second trajectories are short, and the third trajectory is long. The asymmetry of the trajectories is then used to determine the flow direction. We first evaluate the detection rate by increasing the trajectory density of synthetic images. The image size was fixed at 500 9 500 pixels, and the number of trajectories was increased from 28 to 280, and the detection rate was examined. Then, we evaluated the accuracy of detection of the center of gravity of the first and last trajectories using the root mean square error. Finally, we used the coded illumination method to visualize the swirling flow inside a device to examine its applicability to real flows. Keywords Particle streak velocimetry Particle tracking velocimetry Particle image velocimetry Feature matching
1 Introduction Particle tracking velocimetry (PTV) is used to measure the velocity of objects from images with single exposure and multiple frames with short time intervals. Although this method has high position accuracy, particle matching is difficult, and various tracking algorithms have been proposed to address this problem, including the four time steps PTV (Chang and Tatterson 1983; Kobayashi et al. 1989; Sata et al. 1990; Watanabe et al. 1987), binary cross-correlation method (Uemura et al. 1990), triple pattern matching method (Nishino and Torii 1993), and spring model method (Okamoto et al. 1995). On the contrary, particle streak velocimetry (PSV) is used to measure the velocity of objects from images with continuous illumination and a single frame. Therefore, particle tracking and the timing control of frames is easier with PSV than with PTV. When using PSV with single-frame images, a digital single-lens reflex camera can be used, which has a resolution higher than a high-speed camera. However, the flow direction is ambiguous, and various methods have been proposed to solve this problem (Kobayashi et al. 1983; Kobayashi and Yoshitake 1985; Fujiwara et al. 1987; Murata et al. 1990; Shigematsu and Kohno 2006, 2007). Khalighi and Lee (1989)
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