Michelle R Greene, Benjamin J Balas, Mark D Lescroart, Paul R MacNeilage, Jennifer A Hart, Kamran Binaee, Peter A Hausamann, Ronald Mezile, Bharath Shankar, Christian B Sinnott, Kaylie Capurro, Savannah Halow, Hunter Howe, Mariam Josyula, Annie Li, Abraham Mieses, Amina Mohamed, Ilya Nudnou, Ezra Parkhill, Peter Riley, Brett Schmidt, Matthew W Shinkle, Wentao Si, Brian Szekely, Joaquin M Torres, Eliana Weissmann
{"title":"视觉体验数据集:超过 200 个小时的综合眼球运动、里程测量和自我中心视频记录。","authors":"Michelle R Greene, Benjamin J Balas, Mark D Lescroart, Paul R MacNeilage, Jennifer A Hart, Kamran Binaee, Peter A Hausamann, Ronald Mezile, Bharath Shankar, Christian B Sinnott, Kaylie Capurro, Savannah Halow, Hunter Howe, Mariam Josyula, Annie Li, Abraham Mieses, Amina Mohamed, Ilya Nudnou, Ezra Parkhill, Peter Riley, Brett Schmidt, Matthew W Shinkle, Wentao Si, Brian Szekely, Joaquin M Torres, Eliana Weissmann","doi":"10.1167/jov.24.11.6","DOIUrl":null,"url":null,"abstract":"<p><p>We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240 hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article 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 use and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings.</p>","PeriodicalId":49955,"journal":{"name":"Journal of Vision","volume":"24 11","pages":"6"},"PeriodicalIF":2.0000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11466363/pdf/","citationCount":"0","resultStr":"{\"title\":\"The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.\",\"authors\":\"Michelle R Greene, Benjamin J Balas, Mark D Lescroart, Paul R MacNeilage, Jennifer A Hart, Kamran Binaee, Peter A Hausamann, Ronald Mezile, Bharath Shankar, Christian B Sinnott, Kaylie Capurro, Savannah Halow, Hunter Howe, Mariam Josyula, Annie Li, Abraham Mieses, Amina Mohamed, Ilya Nudnou, Ezra Parkhill, Peter Riley, Brett Schmidt, Matthew W Shinkle, Wentao Si, Brian Szekely, Joaquin M Torres, Eliana Weissmann\",\"doi\":\"10.1167/jov.24.11.6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240 hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. 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The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.
We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240 hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article 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 use and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings.
期刊介绍:
Exploring all aspects of biological visual function, including spatial vision, perception,
low vision, color vision and more, spanning the fields of neuroscience, psychology and psychophysics.