  Annolid is a deep learning-based software package designed for the
segmentation, labeling, and tracking of research targets within video files,
focusing primarily on animal behavior analysis. Based on state-of-the-art
instance segmentation methods, Annolid now harnesses the Cutie video object
segmentation model to achieve resilient, markerless tracking of multiple
animals from single annotated frames, even in environments in which they may be
partially or entirely concealed by environmental features or by one another.
Our integration of Segment Anything and Grounding-DINO strategies additionally
enables the automatic masking and segmentation of recognizable animals and
objects by text command, removing the need for manual annotation. Annolid's
comprehensive approach to object segmentation flexibly accommodates a broad
spectrum of behavior analysis applications, enabling the classification of
diverse behavioral states such as freezing, digging, pup huddling, and social
interactions in addition to the tracking of animals and their body parts.
