EAGER: Collecting Training Videos for Location Estimation with Mechanical Turk

基于位置的服务在在线世界中迅速获得吸引力，因为它们允许高度个性化的服务，以及更容易检索和组织多媒体。然而，这种服务需要准确的地理位置信息(地理标签)来与例如视频的多媒体数据相关联。因为只有一小部分可用视频数据被地理标记。因此，人们对自动估计不包括地理位置元数据的给定视频的地理位置的系统越来越感兴趣。虽然机器学习提供了一种潜在的方法来训练自动位置估计器，但它需要一个标准化的地理标记视频训练语料库。视频的自动收集导致了对机器容易处理的视频的偏向，以及对当前语料库中过多表示的地理位置的偏向。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奖助金(AGERGE)项目探索了一种新颖的，有点高风险的方法来收集这样一个带注释的培训语料库的地理标记视频使用机械土耳其(http://www.mturk.com)，)，一个“工作市场”，吸引来自世界各地的所需专业知识的工人工作在特定的任务，在这种情况下，参加一个游戏，涉及到注释视频与地理位置元数据，例如，全球定位系统坐标。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。从这一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基础。将向研究界提供所产生的数据集和基准，以便能够对替代方法(例如，从视频预测地理位置信息的机器学习算法)进行详细和系统的比较分析。标准化地理标记多媒体数据集的可获得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。由此产生的在地理标记多媒体数据方面的进展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.