A collaborative approach for semantic time-based video annotation using gamification

Abstract

Efficient access to large scale video assets, may it be our life memories in our hard drive or a broadcaster archive which the company is eager to sell, requires content to be conveniently annotated. Manually annotating video content is, however, an intellectually expensive and time-consuming process. In this paper we argue that crowdsourcing, an approach that relies on a remote task force to perform activities that are costly or time-consuming using traditional methods, is a suitable alternative and we describe a solution based on gamification mechanisms for collaboratively collecting timed metadata. Tags introduced by registered players are validated based on a collaborative scoring mechanism that excludes erratic annotations. Voting mechanisms, enabling users to approve or refuse existing tags, provide an extra guarantee on the quality of the annotations. The sense of community is also created as users may watch the crowd's favourite moments of the video provided by a summarization functionality. The system was tested with a pool of volunteers in order to evaluate the quality of the contributions. The results suggest that crowdsourced annotation can describe objects, persons, places, etc. correctly, as well as be very accurate in time.