應用於虛擬甘美朗之手部追蹤辨識系統;HAND TRACKING AND GESTURE RECOGNITION FOR PLAYING VIRTUAL GAMELAN

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题名:	應用於虛擬甘美朗之手部追蹤辨識系統;HAND TRACKING AND GESTURE RECOGNITION FOR PLAYING VIRTUAL GAMELAN
作者:	何迪亞;Aditya, Wisnu
贡献者:	資訊工程學系在職專班
关键词:	手跟踪;手勢識別;深度數據;即時的;DBSCAN;虛擬Gamelan;Hand Tracking;Gesture Recognition;Depth Data;Real-Time;DBSCAN;Virtual Gamelan
日期:	2017-07-28
上传时间:	2017-10-27 14:35:25 (UTC+8)
出版者:	國立中央大學
摘要:	居住在現代社會的人通常會以程度來忘記自己的文化，他們更喜歡現代的東西而不是傳統的。在這種情況下，不能不斷地留下來，因為如果連續一天傳統文化將會消失，所以我們需要保持傳統文化的創新和創新的方式。印度尼西亞的一種傳統表演藝術是六世紀以來的加梅蘭（Gamelan）。結合傳統文化與現代技術有望解決這個問題。該組合以虛擬gamelan系統的形式實現。該系統使用手勢實時控制，使其看起來像原始gamelan玩。使用手勢玩gamelan為gamelan的玩家提供了一個新的體驗。使用手勢提供可選的智能和自然的方式來接口用於人機通信的工具。手分割和跟踪是任何手勢識別應用程序中最大的問題，它們為後續的手勢識別算法提供了最重要的輸入。使用深度數據可以加快分割過程，因為深度數據具有可以識別對象位置的信息，因此我們可以輕鬆地分離對象和背景。我們使用閾值法進行分割，該閾值將能夠減少要處理的數據量，從而加快計算過程。在這項研究中，我們提出了基於密度的空間聚類應用與噪聲（DBSCAN）的數據聚類算法。這種方法用於手跟踪和手勢識別。手跟踪過程使用DBSCAN獲得手級，DBSCAN預計將生成代表右手和左手的兩個類。但是，這兩個類需要進行標記，以便在下一個幀中不改變類。使用距離測量方法的其他手勢。從當前幀中的類與前一幀之間的手中心的位置獲得距離。最後，我們做了一些實驗來獲得DBSCAN的最佳參數，這個參數將產生最好的結果。然後我們通過玩各種姿勢來測試系統。使用DBSCAN的手勢的平均精度為92％。結果表明，我們的方法在虛擬gamelan系統上表現良好.;People who live in modern society usually forget their culture by degrees, they prefer about modern thing rather than traditional. It cannot be left continuously in this kind of situation, because if it continuous someday traditional culture will vanish, so we need to preserve our traditional culture a creative and innovative way. One kind of the traditional performing arts from Indonesia is Gamelan since the 6th century. Combining the traditional culture and modern technologies is expected to solve this problem. This combination is implemented in the form of a virtual gamelan system. The system is controlled in real-time using hand gestures to make it look like the original gamelan play. Using gesture for playing gamelan provide a new experience to the players of gamelan. The use of a hand gesture offering an optional intelligent and natural way to interface tools for human computer communication. Hand segmentation and tracking are the biggest issues in any hand-gesture recognition application and they provide the most vital input for the succeeding gesture recognition algorithm. Using depth data can speed up the process of segmentation because the depth data has information that can recognize the position of an object, then we can separate objects and backgrounds easily. We do segmentation using the threshold method, this threshold will be able to reduce the amount of data to be processed so as to speed up the computation process. In this research, we propose Density-based spatial clustering of applications with noise (DBSCAN) for a data clustering algorithm. This method used in both hand tracking and hand gesture recognition. Hand tracking process uses DBSCAN to obtain hand classes, DBSCAN is expected to produce two classes representing the right hand and left hand. However, these two classes need to be labeled so that no class changes in the next frame. Other gestures using distance measurement methods. The distance is obtained from the position of the hand center between the classes in the current frame with the previous frame. Finally, we did some experiments to get the best parameters for DBSCAN, this parameter will produce the best result. Then we tested the system by playing in various poses. The average accuracy of hand gesture that using DBSCAN is 92%. The results show that our method performs well on a virtual gamelan system.
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