Method for positioning three-dimensional human body joints in monocular color videos

Abstract

The invention provides a method for positioning three-dimensional human body joints in monocular color videos. The method comprises the following steps of: S1, constructing a configurable depth model and importing time sequence information in the depth model; S2, collecting training samples and learning parameters of the depth model by utilizing the training samples; and S3, initializing the depth model by utilizing the parameters learnt in S2, and converting monocular color video data needing three-dimensional human body joint positioning into a plurality of frames of continuous two-dimensional images, inputting the images into the depth model to carry out analysis, and aiming at each frame of two-dimensional image, outputting three-dimensional human body joint coordinates of figures in the image. According to the method, a deep-level convolutional neural network is constructed by utilizing deep learning so as to automatically learn effective spatial-temporal features from a lot of training samples without depending on prior conditions of artificial design and human body joint structural constraints; and through the learnt effective features, the human body joint positions are directly regressed.

Description

technical field [0001] The invention relates to the fields of three-dimensional human body posture recognition, computer vision, pattern recognition and human-computer interaction, in particular to a three-dimensional human body joint point positioning method based on a convolutional neural network and a long-short-term memory network for a monocular color video. Background technique [0002] Pose estimation is an important field of computer vision research. Its main task is to enable the computer to automatically perceive the "where" of the person in the scene and judge the "what" of the person. Its applications include intelligent monitoring, patient monitoring and some human-related computer-interactive system. The goal of human pose is to be able to automatically infer the pose parameters (for example, joint point coordinates) of various parts of the human body from an unknown video (for example, an image frame). Through these pose parameters, the human body's movements...

AI Technical Summary

Problems solved by technology

[0007] Most of the existing 3D pose recognition methods rely on artificially designed prior conditions and human joint structure constraints, but this method evaluates each internal module and cannot be expanded according to specific scenarios

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