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Gesture estimation method based on parallel convolution neural network

A convolutional neural network and gesture technology, applied in the field of gesture estimation, can solve complex problems, achieve the effects of improving robustness, improving efficiency, and saving training time

Active Publication Date: 2017-12-01
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

However, this approach is complex and generally requires multiple assumptions
At the same time, it also requires the design of criteria for evaluating the degree of matching between the depth map and the 3D model, but establishing this criterion is not a simple task

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  • Gesture estimation method based on parallel convolution neural network
  • Gesture estimation method based on parallel convolution neural network

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[0042] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0043] refer to Figure 1 to Figure 6 , a gesture estimation method based on a parallel convolutional neural network, comprising the following steps:

[0044] 1) Image acquisition: acquire the color map and depth map including the human hand through the RGB-D camera;

[0045] 2) Image segmentation: Use a fixed selection frame to scan the color image pyramid with a fixed step size, calculate th...

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Abstract

The invention belongs to the technical field of automatic identification and discloses a gesture estimation method based on a parallel convolution neural network. The method comprises the steps of (S1) using an RGB-D camera to obtain a color image and a depth image, (S2) identifying a position of a hand in the color image according to a hand classification module obtained through training in advance, (S3) finding a corresponding position of the hand in the depth image according to the corresponding position of the hand in the color image, (S4) converting a separated depth image into a gray image, and then inputting the gray image into a parallel convolution neural network gesture estimation model to carry out identification so as to obtain multiple joint coordinates which can express hand gestures, and (S5) outputting a gesture result. The invention provides an end-to-end gesture estimation network architecture, the complexity of an algorithm is reduced, the network convergence speed is greatly improved by a parallel structure, the training time is greatly saved, and improves the efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of automatic recognition, and more specifically relates to a gesture estimation method. Background technique [0002] Gesture estimation is used in many fields, such as sign language recognition, human-computer interaction, and augmented reality. Its purpose is to estimate the coordinates of several joint points of the hand through images. Different from general gesture recognition, gesture estimation is to estimate the coordinates of several joint points of the hand, rather than simply classifying gestures, so it is more technically difficult. With the rise of consumer-grade depth cameras, such as Kinect, a new wave of research based on depth sensors has been triggered. However, there are still many challenges for gesture estimation, mainly for the following reasons: 1. Since the hand joints have multiple degrees of freedom, the gesture posture belongs to a high-dimensional space; 2. The similarity between...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06T7/207
CPCG06T7/207G06T2207/20016G06T2207/20084G06T2207/20081G06T2207/10028G06T2207/10024G06V40/113G06F18/2411
Inventor 胡友民胡中旭吴波刘颉肖玲王诗杰李雪莲武敏健
Owner HUAZHONG UNIV OF SCI & TECH
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