Egocentric vision in-the-air hand-writing and in-the-air interaction method based on cascade convolution nerve network

A technology of convolutional neural network and interaction method, applied in the field of first-view air handwriting and air interaction, can solve problems such as gesture recognition and fingertip detection, and achieve the effect of reducing time and performance consumption

Active Publication Date: 2016-06-29
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, provide a first-view aerial handwriting and aerial interaction method based on cascaded convolutional neural networks, and solve the problems of fi

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  • Egocentric vision in-the-air hand-writing and in-the-air interaction method based on cascade convolution nerve network
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  • Egocentric vision in-the-air hand-writing and in-the-air interaction method based on cascade convolution nerve network

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Embodiment

[0058] The present invention is based on the cascaded convolution deep convolutional neural network first-view air handwriting and air interaction method, such as image 3 shown, including the following steps:

[0059] S1. Obtain training data, manually mark the upper left corner point and the lower right corner point of the circumscribed rectangle (foreground area) containing the hand area, manually mark the coordinates of the fingertip, and manually mark the categories of different gestures;

[0060] S1.1 simulates the first perspective by using the camera at the position of the human eye (such as Figure 1(a)-Figure 1(b) shown), collect large-scale data, cover different scenarios, including many different gestures (such as figure 2 shown), one of which must be a single-finger gesture with a visible fingertip;

[0061] S1.2 Manually mark the coordinates of the upper left corner and the lower right corner of the circumscribed rectangle of the hand area, manually mark the ge...

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Abstract

The invention discloses an egocentric vision in-the-air hand-writing and in-the-air interaction method based on a cascade convolution nerve network. The method comprises steps of S1: obtaining training data; S2: designing a depth convolution nerve network used for hand detection; S3: designing a depth convolution nerve network used for gesture classification and finger tip detection; S4: cascading a first-level network and a second-level network, cutting a region of interest out of a foreground bounding rectangle output by the first-level network so as to obtain a foreground region including a hand, and then using the foreground region as the input of the second-level convolution network for finger tip detection and gesture identification; S5: judging the gesture identification, if it is a single-finger gesture, outputting the finger tip thereof and then carrying out timing sequence smoothing and interpolation between points; and S6: using continuous multi-frame finger tip sampling coordinates to carry out character identification. The invention provides an integral in-the-air hand-writing and in-the-air interaction algorithm, so accurate and robust finger tip detection and gesture classification are achieved, thereby achieving the egocentric vision in-the-air hand-writing and in-the-air interaction.

Description

technical field [0001] The present invention relates to the fields of computer vision and machine learning, in particular to a first-view aerial handwriting and aerial interaction method based on a cascaded convolutional neural network. Background technique [0002] In recent years, with the rise of virtual reality technology (Virtual Reality, VR) and augmented reality technology (Augmenting Reality, AR), the first perspective (EgocentricVision) gesture interaction technology has attracted extensive attention from academia and industry, especially Google Glass, Microsoft Hololens, etc. The emergence of smart wearable devices and virtual reality devices such as Oculus makes it difficult to apply traditional human-computer interaction methods. An algorithm is urgently needed to help devices understand human interaction needs, such as gesture operations. Gesture interaction technology mainly involves two aspects, gesture recognition and key point positioning. The present invent...

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Application Information

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IPC IPC(8): G06K9/00G06N3/08
CPCG06N3/084G06V40/28
Inventor 金连文黄毅超刘孝睿张鑫
Owner SOUTH CHINA UNIV OF TECH
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