由於視訊資料大量數位化，數位資料暴增，但若結合各種最新之壓縮及視訊處理標準，編碼及壓縮的目標不難達成，所以現階段最急於解決的是我們如何在最短的時間內找到最符合我們需求的Content，也就是說，如何快速地、有效率地存取了斛我們所要的數位資料成為一項重要的課題。因此，我的研究主要是去建立一個能夠認知、分類及總結視訊資料的系統，我限定的應用範疇在足球影片的視訊資料，因為運動節目具有高重覆性，高相關性的鏡頭特性，對我在做分析時較有利。
我的論文方向是利用動態貝氏網路(Dynamic Bayesian Network)的架構，推演出不易直接觀測到的高階語意特徵，例如，射門、角球、十二碼罰球、犯規鏡頭等，進而推論出視訊的高階特性來做來做運動節目的語意認知。也就是動態貝氏網路可以建構起高低特徵的橋梁和結合不同的特徵資訊，進而達到semantic analysis 的目的。測試視訊先經過我們所設計的各種特微分析器求得一些低階的資訊，我們把些資訊視為動態貝氏網路的輸入，籍由在訓練程序所求得的一些機率分佈，向上推演得到我們所要的類別資訊。除了單純考慮一個時間點的貝氏網路外，我也希望利用貝氏網路時間上的相關性(time dependency)，也就是動態貝氏網路來判斷視訊的特性，如此可以得到較好的結果。

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