Automated Analysis of Bird Head Motion from Videos in Unconstrained Settings

This study introduces a framework applicable to videos of habitual behaviors of multiple bird species to automatically assess head angular velocities and frequencies during various behaviors in natural habitats. 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, including Animal Kingdom, NABirds, Birdsnap, CUB-200-2011, and eBird, featuring videos and images of diverse bird species in uncontrolled settings. Initial challenges arose due to the complexity of video backgrounds, leading to misidentifications and inaccurate key point estimates. These issues were addressed through validation, refinement, filtering, and smoothing steps. 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 eBird 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.