Gesture recognition using cameras capable of capturing detailed images for gesture recognition is not feasible in many places due to concerns regarding privacy and information leakage. To address this problem, we have proposed a method of capturing shadow pictures using single-pixel-imaging to realize privacy-conscious gesture recognition. As an implementation method of single-pixel-imaging in public spaces, we have studied using a high-frame-rate LED display as a light source. By using a high-frame-rate LED display, random patterns can be latent while the observer perceives an apparent image. However, the image reconstructed by single-pixel-imaging using a high-frame-rate LED display is influenced by the apparent image, making gesture recognition difficult. In this study, we show that the influence of the apparent image can be removed by restoring the restored image using deep learning with a convolutional network called U-Net, and high classification accuracy with a small number of illuminations by using LeNet to classify restored images.

由于担心隐私和信息泄漏，使用能够捕获详细图像进行手势识别的相机进行手势识别在许多地方并不可行。为了解决这个问题，我们提出了一种使用单像素成像捕获阴影图片的方法，以实现注重隐私的手势识别。作为公共空间单像素成像的实现方法，我们研究了使用高帧率LED显示屏作为光源。通过使用高帧率 LED 显示屏，当观察者感知到明显的图像时，随机图案可能会隐藏起来。然而，使用高帧率LED显示屏进行单像素成像重建的图像受到视在图像的影响，使得手势识别变得困难。在本研究中，我们表明，通过使用深度学习和称为 U-Net 的卷积网络来恢复恢复的图像，可以消除表观图像的影响，并且通过使用 LeNet 对恢复的图像进行分类，可以在少量照明下获得较高的分类精度。