Real-Time Hand Motion Parameter Estimation with Feature Point Detection Using Kinect  

作　　者：Chun-Ming Chang Che-Hao Chang Chung-Lin Huang 

机构地区：[1]the Department of Applied Informatics and Multimedia, Asia University [2]the Department of Informatics and Multimedia Asia University

出　　处：《Journal of Electronic Science and Technology》2014年第4期429-433,共5页电子科技学刊（英文版）

基　　金：supported by NSC under Grand No.101-2221-E-468-030

摘　　要：This paper presents a real-time Kinect- based hand pose estimation method. Different from model-based and appearance-based approaches, our approach retrieves continuous hand motion parameters in real time. First, the hand region is segmented from the depth image. Then, some specific feature points on the hand are located by the random forest classifier, and the relative displacements of these feature points are transformed to a rotation invariant feature vector. Finally, the system retrieves the hand joint parameters by applying the regression functions on the feature vectors. Experimental results are compared with the ground truth dataset obtained by a data glove to show the effectiveness of our approach. The effects of different distances and different rotation angles for the estimation accuracy are also evaluated.This paper presents a real-time Kinect- based hand pose estimation method. Different from model-based and appearance-based approaches, our approach retrieves continuous hand motion parameters in real time. First, the hand region is segmented from the depth image. Then, some specific feature points on the hand are located by the random forest classifier, and the relative displacements of these feature points are transformed to a rotation invariant feature vector. Finally, the system retrieves the hand joint parameters by applying the regression functions on the feature vectors. Experimental results are compared with the ground truth dataset obtained by a data glove to show the effectiveness of our approach. The effects of different distances and different rotation angles for the estimation accuracy are also evaluated.

关 键 词：Hand motion KINECT PARAMETERESTIMATION random forest regression function. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN911.7[自动化与计算机技术—计算机科学与技术]