Management of crowdsourced first-person video
We present a framework for large-scale crowdsourcing of first-person viewpoint videos recorded on mobile devices. Collecting videos at a massive scale poses a number of major issues in terms of network planning. To improve the scalability with regards to the number of users, videos and geographical area and better cope with restrictions on storage, bandwidth and processing power, the framework is distributed and based on the two-layer cloudlet architecture. To mitigate the limited bandwidth in the access network, a set of decision algorithms is constructed and evaluated that are able to filter out irrelevant videos based on their metadata and given selection criteria. To illustrate the crowdsourcing framework, we present Street View Live, an application for presenting videos based on location, similar to the popular Google Street View but with up-to-date videos covering the location instead of possibly outdated images. In order to have an up-to-date view of every location, the video collection is continuously extended and updated by crowdsourcing videos from mobile devices.