A deep learning approach to track eye movements based on events
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912524836798464 |
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| author | Seth, Chirag Naiken, Divya Lin, Keyan |
| author_facet | Seth, Chirag Naiken, Divya Lin, Keyan |
| contents | This research project addresses the challenge of accurately tracking eye movements during specific events by leveraging previous research. Given the rapid movements of human eyes, which can reach speeds of 300°/s, precise eye tracking typically requires expensive and high-speed cameras. Our primary objective is to locate the eye center position (x, y) using inputs from an event camera. Eye movement analysis has extensive applications in consumer electronics, especially in VR and AR product development. Therefore, our ultimate goal is to develop an interpretable and cost-effective algorithm using deep learning methods to predict human attention, thereby improving device comfort and enhancing overall user experience. To achieve this goal, we explored various approaches, with the CNN\_LSTM model proving most effective, achieving approximately 81\% accuracy. Additionally, we propose future work focusing on Layer-wise Relevance Propagation (LRP) to further enhance the model's interpretability and predictive performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04827 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A deep learning approach to track eye movements based on events Seth, Chirag Naiken, Divya Lin, Keyan Computer Vision and Pattern Recognition 68T05, 68T07 I.2.10; I.5.1; I.4.8; J.4 This research project addresses the challenge of accurately tracking eye movements during specific events by leveraging previous research. Given the rapid movements of human eyes, which can reach speeds of 300°/s, precise eye tracking typically requires expensive and high-speed cameras. Our primary objective is to locate the eye center position (x, y) using inputs from an event camera. Eye movement analysis has extensive applications in consumer electronics, especially in VR and AR product development. Therefore, our ultimate goal is to develop an interpretable and cost-effective algorithm using deep learning methods to predict human attention, thereby improving device comfort and enhancing overall user experience. To achieve this goal, we explored various approaches, with the CNN\_LSTM model proving most effective, achieving approximately 81\% accuracy. Additionally, we propose future work focusing on Layer-wise Relevance Propagation (LRP) to further enhance the model's interpretability and predictive performance. |
| title | A deep learning approach to track eye movements based on events |
| topic | Computer Vision and Pattern Recognition 68T05, 68T07 I.2.10; I.5.1; I.4.8; J.4 |
| url | https://arxiv.org/abs/2508.04827 |