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 × 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.

中文翻译：

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特征匹配轨迹检测算法在粒子条纹测速中的应用

我们使用特征匹配方法检测粒子的轨迹，以提高粒子条纹测速 (PSV) 的分辨率，PSV 用于从可视化路径线测量粒子的速度。与粒子跟踪测速相比，PSV 在粒子匹配方面具有更可靠的性能，因此即使在高密度图像中也不太可能导致错误匹配。获得第一个和最后一个轨迹的重心以计算位移。粒子的轨迹使用二极管激光器照亮，并使用数字单镜头反光相机成像；然后将轨迹分成三个部分，并使用编码照明记录在单个帧中。第一条和第二条轨迹很短，第三条轨迹很长。然后使用轨迹的不对称性来确定流动方向。我们首先通过增加合成图像的轨迹密度来评估检测率。图像尺寸固定为500×500像素，轨迹数从28条增加到280条，检测检出率。然后，我们使用均方根误差来评估检测第一条和最后一条轨迹的重心的准确性。最后，我们使用编码照明方法来可视化设备内的旋流，以检查其对实际流动的适用性。并检查检出率。然后，我们使用均方根误差来评估检测第一条和最后一条轨迹的重心的准确性。最后，我们使用编码照明方法来可视化设备内的旋流，以检查其对实际流动的适用性。并检查了检出率。然后，我们使用均方根误差来评估检测第一条和最后一条轨迹的重心的准确性。最后，我们使用编码照明方法来可视化设备内的旋流，以检查其对实际流动的适用性。