Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments
Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments
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DOI：
10.1007/978-3-319-40902-3_26
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发表时间：
2017-01-01
期刊：
影响因子：
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通讯作者：
Pless, Robert
中科院分区：
文献类型：
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作者：
Hipp, J. Aaron;Adlakha, Deepti;Pless, Robert
公开可用的户外网络摄像头不断查看世界并分享图像。这些摄像头包括交通摄像头、校园摄像头、滑雪度假村摄像头等。许多户外场景档案（AMOS）是一个旨在对这些摄像头和图像进行地理定位、注释、存档和可视化的项目，以作为各种科学应用的资源。自2006年以来，AMOS数据集已经从27，000个网络摄像头中存档了超过7.5亿张户外环境图像。我们的目标是利用AMOS图像数据集和众包开发可靠有效的工具，通过在线、户外网络摄像头捕捉全球身体活动模式和城市建筑环境特征，改善身体活动评估。该项目在捕捉身体活动模式和建筑环境方面的大规模扩大，是在推进使用网络摄像头、众包、最终是机器学习。每30分钟捕获一次户外场景的网络摄像头和提供注释场景的劳动的众包的组合使用允许加速与跨越许多建筑环境的身体活动相关的公共卫生监视。这项公共卫生和计算机视觉合作的最终目标是开发机器学习算法，自动识别和计算身体活动模式。
Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.This project's grand scale-up of capturing physical activity patterns and built environments is a methodological step forward in advancing a real-time, non-labor intensive assessment using webcams, crowdsourcing, and eventually machine learning. The combined use of webcams capturing outdoor scenes every 30 min and crowdsources providing the labor of annotating the scenes allows for accelerated public health surveillance related to physical activity across numerous built environments. The ultimate goal of this public health and computer vision collaboration is to develop machine learning algorithms that will automatically identify and calculate physical activity patterns.