  The analysis of turbulence in plasmas is fundamental in fusion research.
Despite extensive progress in theoretical modeling in the past 15 years, we
still lack a complete and consistent understanding of turbulence in magnetic
confinement devices, such as tokamaks. Experimental studies are challenging due
to the diverse processes that drive the high-speed dynamics of turbulent
phenomena. This work presents a novel application of motion tracking to
identify and track turbulent filaments in fusion plasmas, called blobs, in a
high-frequency video obtained from Gas Puff Imaging diagnostics. We compare
four baseline methods (RAFT, Mask R-CNN, GMA, and Flow Walk) trained on
synthetic data and then test on synthetic and real-world data obtained from
plasmas in the Tokamak `a Configuration Variable (TCV). The blob regime
identified from an analysis of blob trajectories agrees with state-of-the-art
conditional averaging methods for each of the baseline methods employed, giving
confidence in the accuracy of these techniques. High entry barriers
traditionally limit tokamak plasma research to a small community of researchers
in the field. By making a dataset and benchmark publicly available, we hope to
open the field to a broad community in science and engineering.
