你看到我看到的了吗:拍摄场景的众包注释

J. Hipp, Deepti Adlakha, R. Gernes, A. Kargol, Robert Pless
{"title":"你看到我看到的了吗:拍摄场景的众包注释","authors":"J. Hipp, Deepti Adlakha, R. Gernes, A. Kargol, Robert Pless","doi":"10.1145/2526667.2526671","DOIUrl":null,"url":null,"abstract":"The Archive of Many Outdoor Scenes has captured 400 million images. Many of these cameras and images are of street intersections, a subset of which has experienced built environment improvements during the past seven years. We identified six cameras in Washington, DC, and uploaded 120 images from each before a built environment change (2007) and after (2010) to the crowdsourcing website Amazon Mechanical Turk (n=1,440). Five unique MTurk workers annotated each image, counting the number of pedestrians, cyclists, and vehicles. Two trained Research Assistants completed the same tasks. Reliability and validity statistics of MTurk workers revealed substantial agreement in annotating captured images of pedestrians and vehicles. Using the mean annotation of four MTurk workers proved most parsimonious for valid results. Crowdsourcing was shown to be a reliable and valid workforce for annotating images of outdoor human behavior.","PeriodicalId":124821,"journal":{"name":"International SenseCam & Pervasive Imaging Conference","volume":"51 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Do you see what I see: crowdsource annotation of captured scenes\",\"authors\":\"J. Hipp, Deepti Adlakha, R. Gernes, A. Kargol, Robert Pless\",\"doi\":\"10.1145/2526667.2526671\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Archive of Many Outdoor Scenes has captured 400 million images. Many of these cameras and images are of street intersections, a subset of which has experienced built environment improvements during the past seven years. We identified six cameras in Washington, DC, and uploaded 120 images from each before a built environment change (2007) and after (2010) to the crowdsourcing website Amazon Mechanical Turk (n=1,440). Five unique MTurk workers annotated each image, counting the number of pedestrians, cyclists, and vehicles. Two trained Research Assistants completed the same tasks. Reliability and validity statistics of MTurk workers revealed substantial agreement in annotating captured images of pedestrians and vehicles. Using the mean annotation of four MTurk workers proved most parsimonious for valid results. Crowdsourcing was shown to be a reliable and valid workforce for annotating images of outdoor human behavior.\",\"PeriodicalId\":124821,\"journal\":{\"name\":\"International SenseCam & Pervasive Imaging Conference\",\"volume\":\"51 8\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International SenseCam & Pervasive Imaging Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2526667.2526671\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International SenseCam & Pervasive Imaging Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2526667.2526671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

摘要

许多户外场景档案已经拍摄了4亿张照片。这些摄像机和图像中的许多都是十字路口的,其中一部分在过去七年中经历了建筑环境的改善。我们确定了华盛顿特区的六台摄像机,并将每台摄像机在建筑环境变化之前(2007年)和之后(2010年)拍摄的120张照片上传到众包网站Amazon Mechanical Turk (n=1,440)。五个独特的MTurk工作人员对每张图像进行注释,计算行人、骑自行车的人和车辆的数量。两名训练有素的研究助理完成了同样的任务。MTurk工作人员的信度和效度统计显示,在注释捕获的行人和车辆图像方面存在实质性的一致性。使用四个MTurk工人的平均注释证明了最节省的有效结果。众包被证明是一种可靠和有效的劳动力,用于注释户外人类行为的图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Do you see what I see: crowdsource annotation of captured scenes
The Archive of Many Outdoor Scenes has captured 400 million images. Many of these cameras and images are of street intersections, a subset of which has experienced built environment improvements during the past seven years. We identified six cameras in Washington, DC, and uploaded 120 images from each before a built environment change (2007) and after (2010) to the crowdsourcing website Amazon Mechanical Turk (n=1,440). Five unique MTurk workers annotated each image, counting the number of pedestrians, cyclists, and vehicles. Two trained Research Assistants completed the same tasks. Reliability and validity statistics of MTurk workers revealed substantial agreement in annotating captured images of pedestrians and vehicles. Using the mean annotation of four MTurk workers proved most parsimonious for valid results. Crowdsourcing was shown to be a reliable and valid workforce for annotating images of outdoor human behavior.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Physical activity recognition in free-living from body-worn sensors Do you see what I see: crowdsource annotation of captured scenes MemoryMesh: lifelogs as densely linked hypermedia Exploring the technical challenges of large-scale lifelogging Feasibility of identifying eating moments from first-person images leveraging human computation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1