We introduce the Visual Experience Dataset (VEDB), a compilation of over 240 hours of egocentric video combined with gaze- and head-tracking data that offers an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 58 observers ranging from 6-49 years old. This paper outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset. The VEDB's potential applications are vast, including improving gaze tracking methodologies, assessing spatiotemporal image statistics, and refining deep neural networks for scene and activity recognition. The VEDB is accessible through established open science platforms and is intended to be a living dataset with plans for expansion and community contributions. It is released with an emphasis on ethical considerations, such as participant privacy and the mitigation of potential biases. By providing a dataset grounded in real-world experiences and accompanied by extensive metadata and supporting code, the authors invite the research community to utilize and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings.
@article{arxiv.2404.18934,
title = {The Visual Experience Dataset: Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric Video},
author = {Michelle R. Greene and Benjamin J. Balas and Mark D. Lescroart and Paul R. MacNeilage and Jennifer A. Hart and Kamran Binaee and Peter A. Hausamann and Ronald Mezile and Bharath Shankar and Christian B. Sinnott and Kaylie Capurro and Savannah Halow and Hunter Howe and Mariam Josyula and Annie Li and Abraham Mieses and Amina Mohamed and Ilya Nudnou and Ezra Parkhill and Peter Riley and Brett Schmidt and Matthew W. Shinkle and Wentao Si and Brian Szekely and Joaquin M. Torres and Eliana Weissmann},
journal= {arXiv preprint arXiv:2404.18934},
year = {2024}
}