智財技轉處 Department of Intellectual Property and Technology Transfer 智財技轉處 Department of Intellectual Property and Technology Transfer

KNOW-HOW

手勢偵測其應用範圍廣泛，然而亦為極具挑戰之研究課題。手勢偵測所面臨的挑戰主要在於兩方面。一個是手的自由度大，另一個則是一般手勢所造成的快速移動。一般基於目標函數最小化的演算法，當給定好的初始値時可以在低幀率下獲得不錯的效果。然而這些方法極度倚賴初始點，且手部位置在快速移動下所造成的追蹤損失難以回復。本研究發展一個基於深度學習以訓練手勢的方法，可以用來做為手勢姿勢與角度的粗略估測。

Hand gesture detection has a wide area of application but is quite a challenging task. There are two main problems that make hand gesture detection especially difficult. One is the great number of degrees of freedom of the hand and the other one is the rapid movements that we make in natural gestures. Algorithms based on minimizing an objective function, with a good initialization, typically obtain good accuracy at low frame rates. However, these methods are very dependent on the initialization point, and fast movements on the hand position or gesture, provokes a loss of track which are unable to recover. In this work, we develop a method that uses deep learning to train a set of gestures, which will be used as a rough estimate of the hand pose and orientation.