Abstract
This study presents a framework to automatically analyse head motion in birds from videos of natural behaviours. 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.