EAGER: Collecting Training Videos for Location Estimation with Mechanical Turk

基于位置的服务在网络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，更容易地检索和组织多媒体。但是，这种服务需要精确的地理位置信息（地理标签）与多媒体数据（例如视频）相关联。因为只有一小部分可用的视频数据有地理标记。因此，人们对自动估计给定视频的地理位置而不包括地理位置元数据的系统越来越感兴趣。虽然机器学习提供了一种训练自动位置估计器的潜在方法，但它需要一个标准化的地理标记视频训练语料库。视频的自动收集引入了对容易被机器处理的视频的偏见，以及对当前语料库中过度代表的地理位置的偏见。因此，需要精心策划的标准数据集。这个早期概念探索性研究（EAGER）项目探索了一种新颖的，有点高风险的方法，使用Mechanical Turk （http://www.mturk.com）收集这种带有地理标记的视频的注释培训语料库，这是一个“工作市场”，用于吸引来自世界各地的具有所需专业知识的工人从事特定任务，在这种情况下，参与一个涉及使用地理位置元数据（例如GPS坐标）注释视频的游戏。游戏的用户界面将允许参与者通过点击地图来估计视频的位置。从这种迫切需要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基础。结果数据集和基准将提供给研究界，以便对替代方法进行详细和系统的比较分析（例如，从视频中预测地理位置信息的机器学习算法）。标准化地理标记多媒体数据集的可用性将有助于推动用于地理位置预测的机器学习技术的进步。由此产生的地理标记多媒体数据的进步将使基于位置的智能服务和各种领域成为可能，包括执法、个性化和位置感知媒体检索，用于包括新闻和刑事调查在内的各种应用。

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.