Abstract
The proliferation of mobile devices and the diffusion of social media have changed the communication paradigm of people that share multimedia data by allowing new interaction models (e.g., social networks). In social events (e.g., concerts), the automatic video understanding goal includes the interpretation of which visual contents are the most popular. The popularity of a visual content depends on how many people are looking at that scene, and therefore it could be obtained through the "visual consensus" among multiple video streams acquired by the different users devices. In this work we present RECfusion, a system able to automatically create a single video from multiple video sources by taking into account the popularity of the acquired scenes. The frames composing the final popular video are selected from the different video streams by considering those visual scenes which are pointed and recorded by the highest number of users' devices. Results on two benchmark datasets confirm the effectiveness of the proposed system.
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