在本論文中，我們透過Kinect感應器捕捉動作者的骨架資訊，針對使用者的動態手勢動作，建構了一套基於動態貝氏網路架構的手勢辨識系統，藉由結合在二維空間中左右手移動軌跡的方向以及雙手與人臉三者之間的相對位置關係作為特徵資訊，並以Expectation-maximization (EM)演算法來進行動態貝氏網路模型的參數估測，藉此訓練出一套可適用於單手及雙手動態手勢的辨識系統架構。在實驗的部分，藉由架設Kinect感應器來針對10種常見的排球裁判手勢進行手勢影像資料的蒐集，並對所訓練出來的辨識系統，透過交叉驗證的方式進行實際測試，最後我們依據實驗結果進行分析與比較，說明在該模型架構下，系統對於所蒐集的裁判手勢資料其辨識結果的正確性。

In this thesis, we construct a hand gesture recognition system based on dynamic Bayesian network model through using the human skeleton information captured by Kinect sensor. We estimate the model parameters of the dynamic Bayesian network with Expectation-maximization algorithm and the features including motion direction of both hands and the relative position between both hands and the face, and trained a gesture recognition system which is suitable for both one-hand and two-hand gestures. In the experiments, we focus on 10 common hand gestures of volleyball referee. We trained the hand gesture recognition system with the 1000 video sequences which be collected by Kinect sensor and tested the trained DBN models of hand gestures recognition system with cross-validation. Finally, we analyze and compare the recognition models according to the experimental results, and explain that accuracy of the hand gesture recognition system.

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