EAGER: Collecting Training Videos for Location Estimation with Mechanical Turk

EAGER：使用 Mechanical Turk 收集用于位置估计的培训视频

• 批准号: 1138599
• 负责人: Gerald Friedland
• 金额: $ 5万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1138599&HistoricalAwards=false
• 关键词: EAGER; Collecting; Training; Videos; Location

Location-based services are rapidly gaining traction in the online world as they allow highly personalized services and easier retrieval and organization of multimedia. However, such services require accurate geolocation information (geo-tags) to be associated with the multimedia data e.g., videos. Because only a small fraction of available video data is geo-tagged. Hence, there is a growing interest in systems that estimate the geolocation of a given video automatically that does not include geo-location metadata. While machine learning offers a potential approach to training automatic location estimators, it requires a standardized training corpus of geo-tagged videos. Automatic collection of videos introduces a bias toward videos that are easily processible by machines and towards geographical locations that are over-represented in current corpora. Hence there is a need for carefully curated standard data sets. This EArly-concept Grants for Exploratory Research (EAGER) project explores a novel, somewhat high risk, approach to collecting such an annotated training corpus of geo-tagged videos using Mechanical Turk (http://www.mturk.com), a "marketplace for work" for engaging workers with the desired expertise from around the world to work on a specific task, in this case, participating in a game that involves annotating videos with geolocation metadata e.g., GPS coordinates. The user interface for the game will allow participants to estimate the location of videos by clicking on a map. The knowledge gained from this EAGER would set the stage for more comprehensive geotagged multimedia data collection efforts. The resulting data sets and benchmarks will be made available to the research community to enable detailed and systematic comparative analysis of alternative methods (e.g., machine learning algorithms for predicting geolocation information from videos). The availability of standardized geo-tagged multimedia data sets will help drive advances in machine learning techniques for geo-location prediction. The resulting advances in geo-tagging multimedia data would enable intelligent location based services and a variety of domains including law enforcement, personalized and location-aware media retrieval, for a variety of applications including journalistic and criminal investigations.

基于位置的服务在在线世界中迅速获得了吸引力，因为它们允许高度个性化的服务以及更容易的多媒体检索和组织。但是，此类服务需要准确的地理位置信息（地理标签）与多媒体数据相关联，例如视频。因为只有一小部分可用的视频数据被GEO标记。因此，对系统自动估算给定视频的地理位置的系统越来越感兴趣，该系统不包括地理位置元数据。 当机器学习提供了一种潜在的培训自动位置估计器的方法，但它需要标准化的地理标签视频培训语料库。自动收集视频引入了对视频的偏见，这些视频很容易被机器处理，并且朝着当前语料库中代表过多的地理位置进行处理。因此，需要精心策划的标准数据集。 This EArly-concept Grants for Exploratory Research (EAGER) project explores a novel, somewhat high risk, approach to collecting such an annotated training corpus of geo-tagged videos using Mechanical Turk (http://www.mturk.com), a "marketplace for work" for engaging workers with the desired expertise from around the world to work on a specific task, in this case, participating in a game that involves annotating videos with地理位置元数据，例如GPS坐标。游戏的用户界面将允许参与者通过单击地图估算视频的位置。从这种渴望中获得的知识将为更全面的地理标记多媒体数据收集工作奠定了基础。最终的数据集和基准将提供给研究社区，以实现替代方法的详细和系统的比较分析（例如，用于预测视频中的地理位置信息的机器学习算法）。标准化地理标记的多媒体数据集的可用性将有助于推动地理位置预测的机器学习技术的进步。地理多媒体数据的最终进展将为包括新闻和刑事调查在内的各种应用程序提供基于智能位置的服务以及包括执法，个性化和位置感知媒体检索在内的各种领域。