Introduction

Introduction#

Annolid stands for Annotation + Annelid (segmentation).

Annolid is a toolkit for video-based research workflows that combines:

  • Annotation (polygons, keypoints, and event labels)

  • Instance segmentation and multi-object tracking

  • Keypoint tracking and downstream analysis (e.g., place preference, motion/freezing metrics)

What Annolid can do today#

Annolid’s feature set evolves quickly, but the core workflows are stable:

  • Fast labeling in the GUI (LabelMe-based), with manual tools plus AI-assisted tools.

  • AI-assisted polygons using point prompts (Segment Anything family) and text prompts (Grounding DINO → SAM).

  • Video tracking backends including Cutie / EfficientTAM-style VOS, plus model-driven segmentation/pose options (e.g., YOLO).

  • Export and interoperability: LabelMe JSON, CSV summaries, COCO, and YOLO dataset conversion.

  • Behavior/event utilities: event marking in the GUI, time-budget summaries, and post-hoc analyses.

If you need help or encounter an issue, please open an issue or use the community links in Get in touch.

Video introduction#

Below is a brief introduction to annolid:

Annolid can be applied to many diverse goals#

  • Animal Tracking

  • Keypoints tracking (i.e. body parts)

  • Automated behavior recognition

  • Multiple animal tracking, including periods of partial body occlusion

  • Whole-body masking

  • Automated identification of interactions

Video courtesy of Caitlyn Finton and Alex Ophir:

  • Masking and automatic scoring of lone animals and huddles of multiple animals

  • Confidence of identification reported

Video courtesy of Rikki Laser and Alex Ophir:

  • Animal and object tracking, including periods of occlusion

  • Tracked objects automatically associated with user-defined zones

  • Robustness to noisy background

Video courtesy of Emily Sattora and Christiane Linster:

  • Identification of freezing behavior (e.g., from fear conditioning)

  • Reporting of motion score based on optical flow measurements applied selectively to the body mask

  • Multiple animal tracking on cryptic background

Video courtesy of Jessica Nowicki, Julia Cora-anne Lee, and Lauren O’Connell:

  • Multiple animal tracking with a large field of view

Video courtesy of Santiago Forero and Alex Ophir:

Youtube playlist#

You can find these videos, tutorials on how to best use Annolid as well as exemples in Annolid’s youtube playlist here.

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