A Depth Image Gesture Estimation Method Based on Semi-Supervised Learning

Abstract

The present invention disclosed a method of deep image gesture based on semi -supervised learning.Compared to RGB images, higher accuracy can be obtained from deep images.Although the current method of deep learning gesture estimates is good, it is too dependent on the use of labeling data for training. However, the process of labeling the three -dimensional gestures in the image is very complicated.The present invention proposes a efficient point cloud expression, effectively integrates local characteristics and global characteristics, and realizes a new method of estimating three -dimensional hand posture from deep images.By reducing the dependence of data in the process of model training, the cost of data labeling is reduced.Compared with the previous semi -supervised learning methods, the present invention has achieved a breakthrough in accuracy in accuracy on the premise of ensuring operating efficiency.

Description

technical field [0001] The invention belongs to the technical field of digital image recognition, and more specifically, relates to a method for estimating gestures of depth images based on semi-supervised learning. Background technique [0002] Automatic real-time 3D gesture estimation has received a lot of attention this year, which includes a wide range of application scenarios, such as human-computer interaction, computer graphics and virtual / augmented reality, etc. After years of intensive research, 3D gesture estimation has achieved remarkable progress in accuracy and efficiency. Since convolutional neural networks perform well in processing images, most gesture estimation methods are based on convolutional neural networks. Some methods use 2D convolutions to process depth images, but the features extracted by 2D convolutional neural networks are not suitable for direct 3D pose estimation due to the lack of 3D spatial information representation. To better capture the...

AI Technical Summary

Problems solved by technology

Aiming at the problem that a large amount of labeled gesture information is required in the training process of the gesture estimation model, but the cost of labeling 3D gesture labeling information is high, the present invention designs a semi-supervised deep network architecture, the purpose of which is to use unlabeled data and relatively Use less labeled data to train the entire network to improve the accuracy of 3D gesture estimation and reduce the cost of training data labeling

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