论文标题
使用LEAP运动传感器和卷积神经网络的3D动态手势识别
3D dynamic hand gestures recognition using the Leap Motion sensor and convolutional neural networks
论文作者
论文摘要
在许多应用程序上下文中，定义对手势自动理解的方法至关重要，在虚拟现实应用程序中，可以创建更自然和易于使用的人类计算机交互方法。在本文中，我们提出了一种识别通过LEAP运动传感器获得的一组非静态手势的方法。获得的手势信息在颜色图像中转换，其中手势过程中手动接头位置的变化投影在平面上，时间信息以投影点的颜色强度表示。手势的分类是使用深卷积神经网络（CNN）进行的。采用了流行的Resnet-50体系结构的修改版本，通过删除最后一个完全连接的层并添加与所考虑的手势类一样多的神经元来获得。该方法已成功应用于现有参考数据集，并且已经执行了初步测试以实时识别用户执行的动态手势。
Defining methods for the automatic understanding of gestures is of paramount importance in many application contexts and in Virtual Reality applications for creating more natural and easy-to-use human-computer interaction methods. In this paper, we present a method for the recognition of a set of non-static gestures acquired through the Leap Motion sensor. The acquired gesture information is converted in color images, where the variation of hand joint positions during the gesture are projected on a plane and temporal information is represented with color intensity of the projected points. The classification of the gestures is performed using a deep Convolutional Neural Network (CNN). A modified version of the popular ResNet-50 architecture is adopted, obtained by removing the last fully connected layer and adding a new layer with as many neurons as the considered gesture classes. The method has been successfully applied to the existing reference dataset and preliminary tests have already been performed for the real-time recognition of dynamic gestures performed by users.
