  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.
