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Gesture recognition method and device based on graph convolutional neural network

A technology of convolutional neural network and gesture recognition, which is applied in the field of gesture recognition based on graph convolutional neural network, can solve the problems of poor real-time performance, low accuracy of gesture recognition, inconvenient recognition delay of wearable sensors, etc., to improve real-time performance, and the effect of improving accuracy

Inactive Publication Date: 2019-10-29
GUANGDONG UNIV OF TECH
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Problems solved by technology

However, due to the inconvenience and recognition delay of wearable sensors, the most popular research direction is gesture recognition based on visual sensors.
[0004] In the gesture recognition of the current visual sensor, template matching, and the limitations of the probability and statistics model lead to low accuracy and poor real-time performance of gesture recognition

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  • Gesture recognition method and device based on graph convolutional neural network
  • Gesture recognition method and device based on graph convolutional neural network
  • Gesture recognition method and device based on graph convolutional neural network

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Embodiment approach

[0070] Optionally, in another embodiment of the present invention, an implementation of step S102 includes:

[0071] Normalize the value of each finger joint point in the time map and space map in the gesture joint point space-time graph to obtain the data to be calculated for each finger joint point.

[0072] Among them, the normalization process is a way to simplify calculations, that is, the expressions with dimensions are transformed into non-dimensional expressions and become scalars.

[0073] Specifically, since the finger joints change greatly in different frames and angles, it is necessary to normalize the position characteristics of each joint in different frames in the gesture joint point space-time diagram, which is more beneficial to The convergence of the algorithm makes the subsequent calculation process more convenient and accurate.

[0074] S103. Input the data to be calculated into the spatio-temporal graph convolutional neural network-gesture recognition mod...

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Abstract

The invention provides a gesture recognition method and device based on graph convolutional neural network. The method comprises the following steps: obtaining a gesture joint point time-space diagramby using an attitude estimation algorithm, and performing normalization processing on the gesture joint point time-space diagram to obtain data to be calculated, so that data to be calculated can becalculated through an established time-space diagram convolutional neural network-gesture recognition model, and finally, acquiring a recognition result. The calculated data is classified through sixspace-time convolution units, three pooling layers and a support vector machine classifier, thereby improving real-time performance of gesture recognition while improving accuracy of gesture recognition.

Description

technical field [0001] The invention relates to the technical field of video image recognition, in particular to a method and device for gesture recognition based on a graph convolutional neural network. Background technique [0002] Gestures, as a way for primitive humans to communicate, have been used to this day. The importance of conveying information through gestures has not been gradually eliminated with the advancement of time and the development of technology. On the contrary, gestures have become a more important interaction method in the field of human-computer interaction. Gesture recognition technology is currently a research hotspot in the field of computer applications and artificial intelligence. In the fields of robot control, dumb language recognition, unmanned driving and motion detection, gestures give full play to its advantages of convenience, rich meaning, and easy understanding. . [0003] There are two main categories of traditional gesture recognit...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/107G06N3/045G06F18/2411
Inventor 叶典邱卫根陈玉冰刘畅曾博曹祖晟
Owner GUANGDONG UNIV OF TECH
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