本論文提出一個實用的棒球比賽精華片段擷取與分類的方法。由於棒球比賽時間較久，若使用者需要在短時間內瀏覽多場比賽，一個自動的精華片段擷取將帶來許多便利。值得注意的是，精華片段擷取可視為一個數位錄影機的附屬功能，因此，它所耗費的計算資源應該遠少於數位視訊的解壓縮過程以符合實際應用的需求。我們所提出的系統首先將偵測所謂串場效果的位置。由於棒球比賽精彩片段重播出現的前後，通常會由電視台加入串場效果以告知觀眾，我們利用串場效果獨特的視覺特性，準確偵測其位置，即重播畫面出現處。接著，我們以SVM (Support Vector Machine)分類器找出投打對決畫面，作為此段重播實際畫面的起點。再來，我們對於這些精彩畫面以HMM (Hidden Markov Model)加以分析與分類，將內外野精采畫面的情境擷取出來，讓使用者更容易得到所需的內容。本系統主要是建構在MPEG2數位視訊壓縮格式上，即我們有效利用壓縮後的MPEG串流資訊，做為後續分析與處理的主要參考依據。如此設計不僅有效降低系統運算的複雜度，也讓我們的系統與其它已提出的方法相較，更具有實用性。實驗結果顯示，我們所提出的系統具有相當高的準確度。 This paper presents a practical highlight extraction and classification schemes for baseball videos. The approach relies on precise detections of transition effects inserted at the beginning and the end of the replays in the game, which demonstrate the game highlights. It is worth noting that the complexity of the highlight extraction procedure should be limited since it is an auxiliary function of a digital video recorder. Therefore, in the proposed system, the features of MPEG compressed videos are used for subsequent processing to archive efficiency. The properties of transition effects are exploited so that the effects can be accurately retrieved for locating the video segments of replays. Next, the pitching view, which is the starting point of every play in baseball games, will be extracted via Support Vector Machine (SVM). The contents of the play can then be analyzed and classified to determine their types or exciting levels. We classify the extracted highlight segments by using Hidden Markov model (HMM). Experimental results show that the accuracy is good enough to achieve the practical highlight extraction for baseball videos.
