A human posture recognition method based on depth learning

Abstract

The invention discloses a human posture recognition method based on depth learning, which mainly solves the problems of large calculation amount and low accuracy of the current posture recognition technology. Firstly, the kinect V2.0 depth sensor is used to collect the motion and posture characteristics of several human body samples; RGB data and bone data of human motion posture are stored; skeletal images obtained from bone data after image preprocessing are used as training set and test set; the training set is inputted into a Posture based on Convolution Neural Network (CNN), which is specially used in the field of human posture recognition. In CNN, the classification result is obtained after training and adjusting the network structure and network parameters. The motion and posture characteristics of different human body samples are input into the classification network as a test set, and the most probable motion is the recognition result. The invention uses the convolution neuralnetwork to improve the identification accuracy, reduce the identification time, has low operation cost, and is simple and convenient to be applied in places such as intelligent home, safety monitoring, motion analysis and the like.

Description

technical field [0001] The present invention relates to the field of deep learning and biometric feature recognition, in particular to a method for recognizing human body movements and postures based on deep learning. Background technique [0002] With the development of society and the advancement of science and technology, the state and society have given more attention and investment to the field of artificial intelligence technology in recent years. As an important part of the field of artificial intelligence, computer vision also attracts the attention of the majority of people. People hope that computers can have a pair of eyes like humans to understand the actual scene, so as to help humans complete a series of tasks and obtain better human-computer interaction examples. [0003] The most popular direction in the field of computer vision is biometric recognition, which identifies people through video or images, including face recognition, fingerprint recognition, pal...

AI Technical Summary

Problems solved by technology

However, both methods have their own shortcomings. The RGB image contains too much information, which is not conducive to the extraction of gesture features.

In the depth image, it is easy for the limbs to occlude each other, which affects the recognition accuracy.

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