Automated analysis of bird head motion in unconstrained settings: A foundational study on semicircular canal evolution in archosaurs

This study presents a framework to automatically analyze head motion in
birds from videos of natural behaviors. The process involves detecting
birds, identifying key points on their heads, and tracking changes in
their positions over time. Bird detection and key point extraction were
trained on publicly available datasets, featuring videos and images of
diverse bird species in uncontrolled settings. Initial challenges with
complex video backgrounds causing misidentifications and inaccurate key
points were addressed through validation, refinement, filtering, and
smoothing. Head angular velocities and rotation frequencies were computed
from the refined key points. The algorithm performed well at moderate
speeds but was limited by the 30 Hz frame rate of most videos, which
constrained measurable angular velocities and frequencies and caused
motion blur, affecting key point detection. Our findings suggest that the
framework may provide plausible estimates of head motion but also
emphasize the importance of high frame rate videos in future research,
including extensive comparisons against ground truth data, to fully
characterize bird head movements. Importantly, this work is a foundational
effort to understand the evolutionary drivers of the semicircular canals,
the biosensor that monitors head rotations, for both extinct and extant
tetrapods.