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Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense

机译：使用RealSense与广义Hough变换和DC-CNN的手势识别

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Hand gesture recognition plays an important role in human-computer interaction. With the development of depth cameras, color images combined with depth images can provide richer information for hand gesture recognition. In this paper, we propose a hand gesture recognition system based on the data captured by Intel RealSense Front-Facing Camera SR300. Considering that the pixels in depth images collected by RealSense are not one-to-one to those in color images, the recognition system maps depth images to color images based on generalized Hough transform in order to segment hand from a complex background in color images using the depth information. Then, it recognizes different hand gestures by a novel double-channel convolutional neural network containing two input channels which are color images and depth images. Moreover, we built a hand gesture database of 24 different kinds of hand gestures representing 24 letters in the English alphabet. It contains a total of 168,000 images which are 84,000 RGB images and 84,000 depth images. Experimental results on our newly collected hand gesture database demonstrate the effectiveness of the proposed approach, and the recognition accuracy is 99.4%.
机译：手势识别在人机互动中起着重要作用。随着深度摄像机的发展，彩色图像与深度图像相结合可以提供用于手势识别的更丰富的信息。在本文中，我们提出了一种基于由Intel RealSense前置摄像机SR300捕获的数据的手势识别系统。考虑到由真塞收集的深度图像中的像素不是彩色图像中的一对一，识别系统将深度图像映射到基于广义的Hough变换的彩色图像，以便从使用的复杂背景中段手深度信息。然后，它通过包含两个输入通道的新型双通道卷积神经网络识别不同的手势，该内部图像是彩色图像和深度图像。此外，我们建立了24种不同种类手势手势的手势数据库，表示英文字母中的24个字母。它包含总共168,000个图像，它是84,000 RGB图像和84,000个深度图像。我们新收集的手势数据库上的实验结果证明了所提出的方法的有效性，识别准确性为99.4 ％。

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