# idtracker.ai: Tracking all individuals in large collectives of unmarked animals

arXiv: https://arxiv.org/abs/1803.04351 · HTML: https://mlsanity.com/paper/1803.04351/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [From eye to AI: studying rodent social behavior in the era of machine Learning](https://mlsanity.com/paper/2508.04255/) (2025)
- [TrackFish3D: Self-Supervised 3D Tracking of Schooling Fish from Multi-view Videos](https://mlsanity.com/paper/2609.38347/) (2026)

## Abstract (arXiv)

Our understanding of collective animal behavior is limited by our ability to track each of the individuals. We describe an algorithm and software, idtracker.ai, that extracts from video all trajectories with correct identities at a high accuracy for collectives of up to 100 individuals. It uses two deep networks, one detecting when animals touch or cross and another one for animal identification, trained adaptively to conditions and difficulty of the video.