近年來，隨著科技進步日新月異，對於人體行為動作的偵測之研究也越來越普及。本論文提出一套基於狀態轉移支援向量機之可規劃系統用於偵測使用者所定義的人體動作,例如：舉手、起立、坐下、跌倒、蹲下….等，我們提出的系統可用於保全監控、居家看護、互動服務等系統中。
　　本論文主要以狀態轉移支援向量機為辨識基礎，並且提出了可規劃的事件偵測，系統可即時的去抓取動作到訓練樣本中，使用者可依照自己想要的動作去定義，並且將想要定義的動作變成訓練樣本放入辨識器中做訓練，訓練後即可偵測此定義之動作。由於人體的動作都有相關且連續的特性，因此我們提出一個新的方法，將每時間點的影像資訊採用支援向量機辨識出的狀態與下一個時間點所辨識的狀態去做連結，且在每一個時間點上求出該狀態的自身機率加上該狀態與下一個狀態之間的轉移機率值,藉由Viterbi algorithm 求出一條最佳路徑，並求出最佳路徑的機率值。若此機率值超過某一個臨界點，則判斷為該事件。此系統在實驗中求得，在一般環境下正確率平均為82%, 說明此系統是有效且可規劃的。

This thesis comprises of a programmable human behavior detection scheme based on the principal of state transition support vector machine (STSVM). This model is adopted for the classification of human behaviors and the state classification is done by implementing a programmable human behavior detection algorithm. The proposed module utilizes Viterbi’s model to detect the optimal path in state classification. For validating the proposed system, we defined five events such as (1) raising hand; (2) standing up; (3) squatting down; (4) falling down; and (5) sitting.
This system can be programmed by the users and they can abide the rules to train each event classifier, of some human behaviors. Through this programmable approach easy implementation, flexible modification, real time human behavior detection could be highly achievable. Our approach can be accomplished in the ubiquitous computing environment, which we believe as our highlighting feature. Since every event is time-sliced, 10 images per second are initially obtained from web camera. Through the existence of relative
Probability between the current image and next image a transition probability of these images could be found and state probability of each image shall be obtained by STSVM. These probabilities could be utilized to determine an optimal path, which in turns helps in checking all the paths for event detection. Experimental results prove robustness and effectiveness with a precision rate of 82%, which would be is highly suitable for human behavioral detection.

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