基於深度攝影機的混合式手勢辨識

Abstract

手勢辨識成為近年來最具主流的研究是由於在人與機器溝通時，手勢是最自然以及最直接的方式。然而，手勢也廣泛的應用在人機互動上。 在本篇論文中，我們基於深度攝影機前提下提出了一個手勢辨識的方法。首先，運用特定的深度範圍將手部的資訊從背景分離。接著偵測手部的輪廓、手心，以及利用線性迴歸方程式去計算出手掌的大小。再利用手部的輪廓所提供的資訊來預測手勢中每一隻手指的狀態。同時，運用道格拉斯-普克(Douglas-peucker)演算法減少手部輪廓的點使得手部輪廓更平順，再利用這個平順的手部輪廓來預測指尖。最後我們提出了一個手勢種類預測的演算法來辨識手勢。從實驗結果顯示，我們的方法的辨識率介於84.35%到99.55%，而平均的辨識率達到94.29%。

Hand gesture recognition (HRG) becomes one of most popular topics in recent years because that hand gesture is one of the most natural and intuitive way of communication between Human and machines. It is widely used in HCI (Human-Computer-interaction). In this paper, we proposed a method for hand gesture recognition based on depth camera. Firstly, the hand information within depth image is separated from background based on a specific range of depth. And the contour of hand is detected after segmentation. After that, we estimate centroid of hand, and palm size is calculated by using linear regression. Then, fingers’ states of gesture are estimated depending on information of hand contour. And fingertips are estimated by means of smooth hand contours which reduce number of contours by Douglas-Peucker Algorithm. Finally, we propose a gesture type estimation algorithm to determine which gesture is. The extensive experiments demonstrate that the accuracy rate of our method is from 84.35% to 99.55%, and the mean accuracy is 94.29%.