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.

介绍 Visual Experience Dataset（VEDB）：一个由超过 240 小时的自我感知视频结合注视和头部追踪数据组成的数据集，提供了人类观察者在视觉世界中的前所未有的体验。该数据集包含 717 个会话，由 58 名 6-49 岁的观察者记录。介绍数据收集、处理和标记协议，讨论数据集内潜在的错误或偏差来源。VEDB 的应用潜力广泛，包括改进注视追踪方法、评估时空图像统计数据以及优化场景和活动识别的深度神经网络。VEDB 可以通过已建立的开放科学平台访问，旨在成为一个持续更新的数据集，并鼓励社区贡献。数据集强调了参与者隐私和减轻潜在偏差等伦理考虑。通过提供基于真实世界经验的数据集，并附有丰富的元数据和支持代码，作者邀请研究社区利用和贡献 VEDB，以促进对自然环境中视觉感知和行为的更深入理解。