FLAD: a human-centered video content flaw detection system for meeting recordings

Abstract

Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle this issue in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on additional conditions of conference venues, like infrared camera or special microphone, which are not practical. Moreover, current video quality assessment works mainly focus on the quality loss after compression or encoding rather than the human-centered video content flaws. In this paper, we design and implement FLAD, a human-centered video content flaw detection system for meeting recordings, which could build a bridge between subjective sense and objective measures from a human-centered perspective. The experimental results illustrate the proposed algorithms could achieve the state-of-the-art video content flaw detection performance for meeting recordings.