Three-dimensional gesture attitude estimation method

Abstract

The invention discloses a three-dimensional gesture attitude estimation method, comprising: S1) obtaining a plurality of gesture depth images and performing gesture foreground and background segmentation to obtain a plurality of gesture foreground images and to randomly divide the foreground images into a training set and a testing set; S2) according to the gesture model graphs, creating the actual label graphs for the gesture foreground images wherein the actual label graphs contain a plurality of coordinate points representing the various reference identification points of a person's hands in the gesture foreground images with each coordinate point value comprising the coordinate value and the depth value of the corresponding reference identification points; S3) selecting a plurality of gesture foreground images from the training set to train the full-convolution neural network and outputting a plurality of prediction label graphs correspondingly; S4) comparing the deviations between the actual label graphs and the prediction label graphs and at the same time, updating the network parameters; S5) iterating through constantly reducing the deviations until the network parameters are converged; and S6) outputting the to-be-estimated gesture depth graphs and outputting corresponding label graphs as the estimation result.

Description

technical field [0001] The invention relates to the fields of computer vision and deep learning, in particular to a method for estimating a three-dimensional gesture pose. Background technique [0002] In recent years, with the gradual popularization of virtual reality and augmented reality technology and immeasurable development prospects, as an important means of human-computer interaction, gesture recognition technology has been highly concerned by the field of computer vision. It is more complex, has a higher degree of freedom and is prone to occlusion. It has always been a difficult problem to quickly and accurately recognize gesture positions and gesture actions. [0003] Traditional gesture estimation methods can generally be divided into two types: sensor-based and image-based. Sensor-based gesture and posture estimation technology refers to fixing sensors such as accelerometers and angular velocity meters on specific parts of the palm and fingers of a person, so th...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to propose a three-dimensional gesture pose estimation method based on a depth map and a fully convolutional neural network to overcome the low recognition accuracy and volume of existing gesture recognition methods using a deep convolutional neural network. The problem of many training parameters and slow training and testing speed of the product neural network

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