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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.10305 |
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| _version_ | 1866909536695091200 |
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| author | Mededovic, Emil Wu, Yuli Konermann, Henning Kopaczka, Marcin Schulz, Mareike Tolba, Rene Stegmaier, Johannes |
| author_facet | Mededovic, Emil Wu, Yuli Konermann, Henning Kopaczka, Marcin Schulz, Mareike Tolba, Rene Stegmaier, Johannes |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Eye on the Target: Eye Tracking Meets Rodent Tracking Mededovic, Emil Wu, Yuli Konermann, Henning Kopaczka, Marcin Schulz, Mareike Tolba, Rene Stegmaier, Johannes Computer Vision and Pattern Recognition 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. |
| title | Eye on the Target: Eye Tracking Meets Rodent Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.10305 |