基于Leap Motion的虚拟课堂手势交互方法
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国家科技支撑计划子课题(2018YFB1004501); 陕西省重点产业创新链项目(2020ZDLGY07-05); 国家自然科学基金青年项目(61902300)


Gesture Interaction Method in Virtual Classroom Based on Leap Motion
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    摘要:

    随着虚拟现实技术的飞速发展, Leap Motion等体感传感器出现并被广泛地应用在人机交互中. 针对 Leap Motion体感控制器在识别范围边缘识别率低且识别速度慢的问题提出了一种基于深度神经网络的Leap Motion手势交互方法. 该方法在定义的交互手势基础上, 设计了三维交互系统并应用到虚拟场景中. 系统首先通过Leap Motion进行数据捕捉, 对获取到的红外图像采用深度神经网络进行特征提取并实现对手势的分类识别, 然后结合Leap Motion获取的手部坐标前后帧的变化来判断动态手势, 最终结合动态手势完成虚拟场景中的交互功能. 经过实验验证, 本文手势识别方法无论是在识别速度还是识别精度上都优于Leap Motion自带的手势识别方法, 同时在Leap Motion识别范围边界处仍能保持较高的识别率.

    Abstract:

    With the rapid development of virtual reality technology, somatosensory sensors such as Leap Motion appear and are widely used in human-computer interaction. This study proposes a Leap Motion gesture interaction method based on a deep neural network to resolve the problem that the Leap Motion somatosensory controller has a low recognition rate and a slow recognition speed at the edge of its recognition range. In addition to the defined interactive gestures, a three-dimensional interactive system is designed and applied to a virtual scene. Specifically, the system captures data with Leap Motion, uses the deep neural network to extract features from the acquired infrared images, and implements gesture classification and recognition. Then, the changes in the hand coordinates between two adjacent frames acquired by Leap Motion are utilized to determine dynamic gestures. Finally, the interaction function in the virtual scene is fulfilled by investigating the dynamic gestures. Experimental verification shows that the proposed gesture recognition method is superior to the built-in gesture recognition method of Leap Motion in both recognition speed and recognition accuracy. Moreover, it still maintains a high recognition rate at the edge of Leap Motion’s recognition range.

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胡发丽,高全力,王西汉,李庆敏.基于Leap Motion的虚拟课堂手势交互方法.计算机系统应用,,():1-9

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  • 收稿日期:2021-10-30
  • 最后修改日期:2021-12-02
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  • 在线发布日期: 2022-06-01
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