# Eye on the Target: Eye Tracking Meets Rodent Tracking

arXiv: https://arxiv.org/abs/2503.10305 · HTML: https://mlsanity.com/paper/2503.10305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Project Aria: A New Tool for Egocentric Multi-Modal AI Research](https://mlsanity.com/paper/2308.13561/) (2023)
- [EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything](https://mlsanity.com/paper/2312.00863/) (2023)
- [GazeSAM: What You See is What You Segment](https://mlsanity.com/paper/2304.13844/) (2023)
- [Weakly-supervised Medical Image Segmentation with Gaze Annotations](https://mlsanity.com/paper/2407.07406/) (2024)
- [Gaze-Assisted Medical Image Segmentation](https://mlsanity.com/paper/2410.17920/) (2024)
- [Bionic Tracking: Using Eye Tracking to Track Biological Cells in Virtual Reality](https://mlsanity.com/paper/2005.00387/) (2020)
- [No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking](https://mlsanity.com/paper/2502.03907/) (2025)

## Abstract (arXiv)

Analyzing animal behavior from video recordings is crucial for scientific research, yet manual annotation remains labor-intensive and prone to subjectivity. Efficient segmentation methods are needed to automate this process while maintaining high accuracy. In this work, we propose a novel pipeline that utilizes eye-tracking data from Aria glasses to generate prompt points, which are then used to produce segmentation masks via a fast zero-shot segmentation model. Additionally, we apply post-processing to refine the prompts, leading to improved segmentation quality. Through our approach, we demonstrate that combining eye-tracking-based annotation with smart prompt refinement can enhance segmentation accuracy, achieving an improvement of 70.6% from 38.8 to 66.2 in the Jaccard Index for segmentation results in the rats dataset.