人們通常會對一些語意事件（semantic-event）感興趣且希望實現自動化偵測， 所以特定行為 （specific-behavior） 偵測已成為影像監視系統（video surveillance system）中相當重要的一門研究。本論文應用一個基於隱型馬可夫模型（Hidden Markov Model）於游泳池環境偵測泳者特定行為的方法。本研究中，泳者行為由時間軸上一連串靜態影像框（image-frame）所描述，運用包體結構（Convexity-Structure）逼近每張影像框中的泳者像素（swimmer-blob），以取出包體結構的三個重要性質－寬高關係、角度比例、頂點的水平座標比例，來描述該泳者的靜態姿勢。此法使原影像串列轉型為三維向量串列。本研究預先取了2000個於任行為中可能存在之姿勢且取出上述特徵向量，用k-mean法分為6群，編製碼書（codebook）以定義6類blob-type，供訓練/辨識時觀測之swimmer-blob可歸屬到最鄰近的族群，達對照轉碼之目的；此法使上述三維向量串列轉型成一簡單編碼（族碼）串列。於訓練階段，使用代表某行為的影片轉出上述的編碼串列，訓練該行為的隱馬可夫模型；測試階段時再將未知行為影片轉成編碼串列輸入訓練好的隱馬可夫模型，檢視匹配度以推論該片段所屬行為。
本研究針對泳者四種正常特定行為（仰泳、蛙泳、捷泳、蝶泳）與兩種異常行為（水道中掙扎、抓繩求救）進行驗證，實作系統以36段影片訓練後對60段影片驗證，對雙向雙水道正常行為泳者同時判別輸出泳姿，達90%的辨識率，對異常行為亦能有效偵測且辨識。

Semantic-event detection is one of the most important parts in video surveillance system, because people usually interest in some specific-behaviors and desire to detect them automatically. This study apply a HMM (Hidden Markov model)-based methodology to detect some specific-behaviors of a swimmer in a swimming pool. In this study, a behavior is composed of a series of the static image-frames. For each frame, we use a Convexity-Structure to enclose the swimmer’s shape after segmenting the swimmer-blob. With the Convexity-Structure, this study clearly defines the feature parameters as a three-dimensional vector. Firstly, more than 2000 swimmer-blobs are analyzed from the possible static postures captured from each interested behaviors. Then, they are clustered into 6 feature-postures by using k-mean method, and a codebook is created for mapping a swimmer-blob into one of 6 feature blob-types in training/recognition stage. Consequently, the time-sequential blobs are converted to a feature-vector sequence and transformed into a symbolic-sequence by the codebook. Thus a learned HMM can be obtained by this symbolic-sequence from a given specific-behavior. After that, the learned HMM is used to detect the swimmer’s behavior which is normal or abnormal.
We implement a system to automatically recognize four different swimming styles (Backstroke, Breaststroke, Freestyle, and Butterfly) and two abnormal behaviors (Rope grasping for help and struggling in waterway). The system is trained by using 36 known video clips and has been verified in two cases. The first case is the classification of 60 video clips, each containing a specified behavior on 1 or 2 waterways. A 90% recognition rate is obtained. Second case is a more realistic situation which combined both abnormal behaviors.

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