Annolid: Annotate, Segment, and Track Anything You Need

TL;DR

Annolid is a versatile deep learning software that enables automatic, resilient segmentation, labeling, and tracking of animals in videos, supporting complex behavior analysis with minimal manual effort.

Contribution

Annolid integrates cutting-edge segmentation and tracking models with text-based automatic masking, advancing animal behavior analysis in videos.

Findings

Resilient, markerless multi-animal tracking in challenging environments

Automatic segmentation and labeling via text commands

Flexible behavior classification capabilities

Abstract

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.