3,536 research outputs found
Large-Scale Mapping of Human Activity using Geo-Tagged Videos
This paper is the first work to perform spatio-temporal mapping of human
activity using the visual content of geo-tagged videos. We utilize a recent
deep-learning based video analysis framework, termed hidden two-stream
networks, to recognize a range of activities in YouTube videos. This framework
is efficient and can run in real time or faster which is important for
recognizing events as they occur in streaming video or for reducing latency in
analyzing already captured video. This is, in turn, important for using video
in smart-city applications. We perform a series of experiments to show our
approach is able to accurately map activities both spatially and temporally. We
also demonstrate the advantages of using the visual content over the
tags/titles.Comment: Accepted at ACM SIGSPATIAL 201
Automatic tagging and geotagging in video collections and communities
Automatically generated tags and geotags hold great promise
to improve access to video collections and online communi-
ties. We overview three tasks offered in the MediaEval 2010
benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features
Recommended from our members
Location-based and contextual mobile learning. A STELLAR Small-Scale Study
This study starts from several inputs that the partners have collected from previous and current running research projects and a workshop organised at the STELLAR Alpine Rendevous 2010. In the study, several steps have been taken, firstly a literature review and analysis of existing systems; secondly, mobile learning experts have been involved in a concept mapping study to identify the main challenges that can be solved via mobile learning; and thirdly, an identification of educational patterns based on these examples has been done.
Out of this study the partners aim to develop an educational framework for contextual learning as a unifying approach in the field. Therefore one of our central research questions is: how can we investigate, theorise, model and support contextual learning
- …