非監督式主播影像偵測於新聞故事分段之研究

Abstract

建立一個全自動的新聞故事分段系統是一個重要且富挑戰性的工作。一則新聞故事段落是由主播播報與外景採訪所組成，所以若能夠知道主播出現的時段就能將一個連續的新聞影片自動分段。本篇論文提出一個以偵測人臉為基礎的主播偵測方式。首先偵測每張新聞影像的人臉區域，利用人臉區域取出特徵，以此特徵做分群，假設最大群是主播，進而篩選出主播人臉區域。因為只注意影像中有人臉的區域，不會受到複雜背景的影響，沒有偵測鏡頭轉換的問題；還有在判斷那張影像有主播的過程並沒有使用主播影像模型，所以不需要為了每個主播去調整模型。我們以5個小時不同電視台的整點新聞影片進行主播影像偵測與新聞故事切割的實驗，驗證所提的方法能正確的找出主播出現的時段。論文最後更進一步將所提的方法整合至一個已有的新聞系統中並成功的應用在東森晚間新聞故事的切割上。

Building an automatic system for news story segmentation is an important and challenging task. A news story is composed of an anchorperson shot and a news footage shot, we can segment a news video into several stories if we know when the anchorperson shows up. This paper presents a method for anchorperson detection based on face detection. First, detecting human faces region in every news frame. Then, extracting features by the face region, and clustering on all features. Suppose that the biggest cluster is presented for anchorperson。This method would not effected by the complex background because it focuses only on the face region. And because of its unsupervised nature, the algorithm does not need to adjust model for different anchorpersons. The efficacy of the proposed method is tested on 5 h of news programs. Moreover, we integrate the proposed method to an existed news video library system and segmenting on the ETT news programs successfully.