Abstract
Deep learning-based optical flow (DLOF) extracts features in adjacent video
frames with deep convolutional neural networks. It uses those features to
estimate the inter-frame motions of objects at the pixel level. In this
article, we evaluate the ability of optical flow to quantify the spontaneous
flows of MT-based active nematics under different labeling conditions. We
compare DLOF against the commonly used technique, particle imaging velocimetry
(PIV). We obtain flow velocity ground truths either by performing
semi-automated particle tracking on samples with sparsely labeled filaments, or
from passive tracer beads. We find that DLOF produces significantly more
accurate velocity fields than PIV for densely labeled samples. We show that the
breakdown of PIV arises because the algorithm cannot reliably distinguish
contrast variations at high densities, particularly in directions parallel to
the nematic director. DLOF overcomes this limitation. For sparsely labeled
samples, DLOF and PIV produce results with similar accuracy, but DLOF gives
higher-resolution fields. Our work establishes DLOF as a versatile tool for
measuring fluid flows in a broad class of active, soft, and biophysical
systems.