AI

Crowdsourcing Event Detection in YouTube Videos

Abstract

Considerable efforts have been put into making video content on the Web more accessible, searchable, and navigable by research on both textual and visual analysis of the actual video content and the accompanying metadata. Nevertheless, most of the time, videos are opaque objects in websites. With Web browsers gaining more support for the HTML5 <video> element, videos are becoming first class citizens on the Web. In this paper we show how events can be detected on-the-fly through crowdsourcing (i) textual, (ii) visual, and (iii) behavioral analysis in YouTube videos, at scale. The main contribution of this paper is a generic crowdsourcing framework for automatic and scalable semantic annotations of HTML5 videos. Eventually, we discuss our preliminary results using traditional server-based approaches to video event detection as a baseline.