A Human Pose Estimation Method Based on Joint Relationship

Abstract

The present invention relates to a human body posture estimation method based on joint relationship, comprising the following steps: S1: building a joint relationship module, including two sub-modules, a channel-based feature relationship module and an adjacent joint space relationship module; S2: building a human body based on joint relationship Pose estimation model; S3: use the labeled human pose data to train the human pose estimation model based on the joint relationship; S4: use the trained human pose estimation model with the joint relationship module to perform the human pose estimation task based on a single image, and obtain the prediction human body posture. Compared with the prior art, the present invention effectively overcomes the problem that it is difficult to detect the position of limb joints with high degrees of freedom such as wrist joints, ankle joints and hidden invisible joints in the image, and the accuracy of human body posture estimation is high.

Description

technical field [0001] The invention relates to the field of human body pose estimation, in particular to a method for estimating human body pose based on joint relationships. Background technique [0002] Human pose estimation is a traditional task in the field of computer vision. Human pose estimation includes the detection of human key points and the generation of human poses. The "key points" in human key point detection refer to important joints such as the top of the human body, shoulders, elbow joints, wrist joints, and ankle joints. The generated human body posture is the complete human skeleton information. With the innovation of computer vision technology, human pose estimation has also gone through a process from manually extracting features to using deep convolutional neural networks as a tool. In recent years, the development of basic deep convolutional neural network structure and performance has also greatly improved the level of extracting human joint featu...

AI Technical Summary

Problems solved by technology

This approach cannot be trained end-to-end, and the graph convolutional network stage is computationally intensive

[0005] The human body is a non-rigid body, and the rotation of each joint has a great degree of freedom. The free rotation of multiple joints can be superimposed on each other. The joints at the far end of the limbs, such as wrist joints and ankle joints, also have multiple degrees of freedom. The location changes, which increases the difficulty of prediction, but the existing methods do not pay attention to the difference in the detection difficulty of different types of joint points brought about by the special structure of the human body; at the same time, in the multi-person pictures that are closer to the real scene, due to Interaction with people, the diversity of positional relationships and the complexity of life scenes, the position of key points of the human body and body information are all facing serious occlusion problems

The occluded human joints are also part of the human pose, and the existing methods do not take targeted optimization measures for the invisible joints that are more difficult to detect correctly

Images