Event-Based Eye Tracking. AIS 2024 Challenge Survey
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| Format: | Preprint |
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2024
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| author | Wang, Zuowen Gao, Chang Wu, Zongwei Conde, Marcos V. Timofte, Radu Liu, Shih-Chii Chen, Qinyu Zha, Zheng-jun Zhai, Wei Han, Han Liao, Bohao Wu, Yuliang Wan, Zengyu Wang, Zhong Cao, Yang Tan, Ganchao Chen, Jinze Pei, Yan Ru Brüers, Sasskia Crouzet, Sébastien McLelland, Douglas Coenen, Oliver Zhang, Baoheng Gao, Yizhao Li, Jingyuan So, Hayden Kwok-Hay Bich, Philippe Boretti, Chiara Prono, Luciano Lică, Mircea Dinucu-Jianu, David Grîu, Cătălin Lin, Xiaopeng Ren, Hongwei Cheng, Bojun Zhang, Xinan Vial, Valentin Yezzi, Anthony Tsai, James |
| author_facet | Wang, Zuowen Gao, Chang Wu, Zongwei Conde, Marcos V. Timofte, Radu Liu, Shih-Chii Chen, Qinyu Zha, Zheng-jun Zhai, Wei Han, Han Liao, Bohao Wu, Yuliang Wan, Zengyu Wang, Zhong Cao, Yang Tan, Ganchao Chen, Jinze Pei, Yan Ru Brüers, Sasskia Crouzet, Sébastien McLelland, Douglas Coenen, Oliver Zhang, Baoheng Gao, Yizhao Li, Jingyuan So, Hayden Kwok-Hay Bich, Philippe Boretti, Chiara Prono, Luciano Lică, Mircea Dinucu-Jianu, David Grîu, Cătălin Lin, Xiaopeng Ren, Hongwei Cheng, Bojun Zhang, Xinan Vial, Valentin Yezzi, Anthony Tsai, James |
| contents | This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Event-Based Eye Tracking. AIS 2024 Challenge Survey Wang, Zuowen Gao, Chang Wu, Zongwei Conde, Marcos V. Timofte, Radu Liu, Shih-Chii Chen, Qinyu Zha, Zheng-jun Zhai, Wei Han, Han Liao, Bohao Wu, Yuliang Wan, Zengyu Wang, Zhong Cao, Yang Tan, Ganchao Chen, Jinze Pei, Yan Ru Brüers, Sasskia Crouzet, Sébastien McLelland, Douglas Coenen, Oliver Zhang, Baoheng Gao, Yizhao Li, Jingyuan So, Hayden Kwok-Hay Bich, Philippe Boretti, Chiara Prono, Luciano Lică, Mircea Dinucu-Jianu, David Grîu, Cătălin Lin, Xiaopeng Ren, Hongwei Cheng, Bojun Zhang, Xinan Vial, Valentin Yezzi, Anthony Tsai, James Computer Vision and Pattern Recognition Artificial Intelligence This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research. |
| title | Event-Based Eye Tracking. AIS 2024 Challenge Survey |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2404.11770 |