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Spatial-temporal improvements of a two-frame particle-tracking algorithm

机译：两帧粒子跟踪算法的时空改进

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摘要

A novel algorithm for particle-tracking velocimetry is proposed and tested with both synthetic and real images. It uses nearest-neighbour cluster matching which performs better than fixed area approaches in terms of spatial adaptivity. The algorithm includes several temporal multi-frame improvements, i.e. extrapolation of the expected particle positions in subsequent frames and the frame-gap technique. To further improve the tracking algorithm performances, the particle identification procedure was modified with respect to the traditional background subtraction, local thresholding and grey level weighted averaging by using the optical flow equation. The local maximum of grey levels around each feature extracted is identified and the barycentres of the particle associated with it are calculated by using Gaussian fitting. The novel algorithm works well with several seeding densities, both homogeneously and inhomogeneously distributed. The multi-frame approach substantially improves the average trajectory length and the number of long trajectories in images with and without noise. The number of barycentres correctly identified by employing the feature extraction is significantly larger than when traditional techniques are used, which in turn increases the number of velocity vectors, allowing a better characterization of the flow field under investigation.
机译：提出了一种新颖的粒子跟踪测速算法，并用合成图像和真实图像进行了测试。它使用最近邻居聚类匹配，在空间适应性方面比固定区域方法表现更好。该算法包括若干时间多帧改进，即，外推后续帧中的预期粒子位置和帧间隙技术。为了进一步提高跟踪算法的性能，通过使用光流方程，针对传统的背景减法，局部阈值化和灰度加权平均对粒子识别过程进行了修改。识别出每个提取的特征周围的局部灰度最大值，并使用高斯拟合来计算与之关联的粒子的重心。新算法在均匀和不均匀分布的几种播种密度下均能很好地工作。多帧方法大大改善了有噪声和无噪声图像中的平均轨迹长度和长轨迹的数量。与使用传统技术相比，通过特征提取正确识别的重心数量明显增加，从而增加了速度矢量的数量，从而可以更好地表征所研究的流场。

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