EAGER: Collecting Training Videos for Location Estimation with Mechanical Turk

International Computer Science Institute, Berkeley CA

Investigators

Abstract

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.