| Summary: | 碩士 === 國立清華大學 === 電機工程學系 === 102 === This thesis presents a real-time and precise depth image based hand pose estimation method. The depth image obtained from Kinect is converted into a feature vector for regression functions to retrieve hand joint parameters. Different from the two mainly proposed methods, model-based and appearance-based, our approach retrieves continuous result within a short period of time. In the beginning, the hand region is segmented from the depth image. Some specific feature points on the hand are located by random forest classifier, and the relative displacements of these feature points would be transformed into a rotation invariant feature vector. Finally, the system retrieves the hand joint parameters by using regression functions which are trained off-line. The results of the proposed method are compared with the ground truth obtained by data glove to evaluate the system reliability. The effects of different distances and different rotation angles to the estimation accuracy are evaluated.
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