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Event-Based Eye Tracking. AIS 2024 Challenge Survey

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arxiv 2404.11770 v1 pith:R742CKTS submitted 2024-04-17 cs.CV cs.AI

Zuowen Wang , Chang Gao , Zongwei Wu , Marcos V. Conde , Radu Timofte , Shih-Chii Liu , Qinyu Chen , Zheng-jun Zha
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Wei Zhai Han Han Bohao Liao Yuliang Wu Zengyu Wan Zhong Wang Yang Cao Ganchao Tan Jinze Chen Yan Ru Pei Sasskia Brüers Sébastien Crouzet Douglas McLelland Oliver Coenen Baoheng Zhang Yizhao Gao Jingyuan Li Hayden Kwok-Hay So Philippe Bich Chiara Boretti Luciano Prono Mircea Lică David Dinucu-Jianu Cătălin Grîu Xiaopeng Lin Hongwei Ren Bojun Cheng Xinan Zhang Valentin Vial Anthony Yezzi James Tsai
This is my paper · ORCID
classification cs.CVcs.AI
keywords challengetrackingevent-basedsurveycameraseventsubmittedtask
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. High-frequency near-eye ground truth for event-based eye tracking

    cs.CV 2025-02 conditional novelty 4.0 of 10

    A semi-automatic pipeline produces 200Hz pupil-center, blink, and saccade annotations for six users in the Angelopoulos event-based eye-tracking dataset.

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