Real-Time 3D Hand Gesture Based Mobile Interaction Interface

Che, Yunlong; Qi, Yue; Song, Yuxiang

doi:10.1109/ismar-adjunct.2019.00-41

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Nowadays, there is a diverse literature in this field. In this context, a mobile app was created by Che, Y. et al in [17] to develop a hand pose estimation method in 3D space to estimate hand poses per frame. In this system, a trained random forest was implemented to provide a rough initial estimate of the hand pose, after which a gradient-based model fitting is implemented.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Lahiani¹,

Néji²

2022

Int. J. Recent Contrib. Eng. Sci. IT

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Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.

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Section: Literature Reviewmentioning

confidence: 99%