因為人們通常會對一些語義事件(semantic event)感興趣並且想去偵測他們，所以語義事件偵測是影像監視系統(video surveillance system)當中最重要的部分之一。這篇論文提出一個偵測特定事件(specific event)的方法。經過前景物體萃取(foreground object extraction)之後，我們得到每張影像畫面的前景遮罩(foreground mask)。我們便利用前景遮罩去計算前景顏色、背景顏色、運動量和運動方向的區塊時間上直方圖(block-based temporal histogram)。每個時間上直方圖用一個參數模型(GMM)模擬，並將GMM的參數當作特徵。利用這些GMM參數訓練一個HMM模型來偵測特定事件。因為直方圖表示法的特性，我們所提出的區塊時間上直方圖不易受到前景萃取所產生的雜訊和錯誤影響。而且，這種區塊時間上直方圖可以保留空間上的資訊，但是一般的直方圖表示法卻有缺少空間資訊的缺點。除了空間資訊，區塊時間上直方圖同時也包含了顏色和運動在時間上變化的資訊。時間資訊的效用將會在實驗結果中證明。

Semantic event detection is one of the most important parts in video surveillance system, because people usually interest in some semantic events and desire to detect them. This thesis proposes a method to detect a specific event. After foreground object extraction, we get foreground mask for each video frame. With the foreground masks, we calculate the block-based temporal histograms for background color, foreground color, motion magnitude, and motion direction. Each temporal histogram is modeled by a parametric model, GMM, and the parameters of GMM are taken as features. With these GMM parameters, an HMM is trained to detect the specific event. Because of the properties of histogram representation, our proposed block-based temporal histogram is insensitive to noise and errors produced in foreground extraction. Furthermore, this block-based temporal histogram retains spatial information, but general histogram representation has the drawback of losing spatial information. Besides spatial information, the block-based temporal histogram incorporates the temporal information of color and motion as well. The effectiveness of temporal information will be proved in experimental results.

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