Hand Gesture Recognition with Leap Motion

被引：1

作者：

Feng, Lin ^{[1
]}

Du, Youchen ^{[1
]}

Liu, Shenglan ^{[1
]}

Xu, Li ^{[2
]}

Wu, Jie ^{[1
]}

Qiao, Hong ^{[3
]}

机构：

[1] Dalian Univ Technol, Dalian, Peoples R China

[2] Neusoft Co Ltd, Shenyang, Peoples R China

[3] Chinese Acad Sci, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE FUTURE TECHNOLOGIES CONFERENCE (FTC) 2018, VOL 1 | 2019年 / 880卷

基金：

中国国家自然科学基金;

关键词：

Hand gesture recognition; Support Vector Machine (SVM); Histogram of Oriented Gradient (HOG); Leap motion;

D O I：

10.1007/978-3-030-02686-8_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Hand gesture is a natural way for people to communicate, it plays an important role in Human-Computer Interaction (HCI). Nowadays, many developers build HCI applications on the top of hand gesture recognition, but how to get more accurate when recognizing hand gestures still have a long way to go. The recent introduction of depth cameras like Leap Motion Controller (LMC) allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with LMC for hand gesture recognition. Histogram of Oriented Gradient (HOG) feature is extracted from Leap Motion binarized and undistorted sensor images. We feed these features into a multi-class Support Vector Machine (SVM) classifier to recognize performed gesture. The results show that our model is much more accurate than previous work.

引用

页码：46 / 54

页数：9

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