Three-dimensional particle tracking velocimetry algorithm based on tetrahedron vote

SerialTrack: ScalE and rotation invariant augmented Lagrangian particle tracking

“…Various algorithms have been created to detect and track individual particles [1] such as the straightforward k-nearestneighbor (kNN) searches, topology-based approaches where neighboring particles are employed to construct local surrounding topology features [27,36,[50][51][52], globally optimized search problems -including linear assignment programming [26], Kalman filtering [53], relaxation methods [19,54], and feature vectorbased techniques [22,25] (see a brief summary of particle tracking open-source codes in Table 2). Among these methods, the nearest neighbor-type search algorithms are typically suitable for relatively low numbers of particles that undergo displacements smaller than the typical interparticle separation distance.…”

SerialTrack: ScalE and Rotation Invariant Augmented Lagrangian Particle Tracking

“…Various algorithms have been created to detect and track individual particles [1] such as the straightforward k-nearest-neighbor (kNN) searches, topologybased approaches where neighboring particles are employed to construct local surrounding topology features [27,36,37,38,39], globally optimized search problems -including linear assignment programming [26], Kalman filtering [40], relaxation methods [19,41], and feature vector-based techniques [22,25] (see a brief summary of particle tracking open-source codes in Table 3). Among these methods, the nearest neighbor-type search algorithms are typically suitable for relatively low numbers of particles that undergo displacements smaller than the typical interparticle separation distance.…”

Cluster-Based Three-Dimensional Particle Tracking Velocimetry Algorithm: Test Procedures, Heuristics and Applications

“…Particle missing and occuring is inevitable in practical situations, so the influence of no-match particles should be treated seriously rather than be neglected. DT based three-dimensional PTV [21] meets the abovementioned heuristics, and the present work is to focus on its last preset parameter: the searching radius R s . To find candidate particles in the second frame which are in a certain range around the target particle from the first frame, a searching radius R s was always used to traverse all particles, to check if their distance to the given coordinate are smaller than R s .…”

“…However, the degree of freedom of either triangle or tetrahedron is so low that when particle intensity is high, clusters become geometrically similar to each other, which is detrimental to PTV judgement. Then the Voronoi Diagram (VD, the dual of DT) was adopted to propose a VD-PTV [20] and its quasi-three-dimensional version [21]. Then the geometrical change of cluster responds sensitively to the inter-frame flow variation, thus leading to a satisfactory matching accuracy.…”