Human body posture estimation method and system based on structure guidance deep learning

Abstract

The invention discloses a human body posture estimation method and system based on structure guidance deep learning, and belongs to the field of computer vision and machine learning. A problem to be solved by the invention is to provide a method for guiding learning by utilizing human skeleton structure knowledge by aiming at shortages of low accuracy and bad robustness caused by the fact that a neural network generally directly predicting human body articulation points does not contain human body structure information. The method mainly comprises input data preprocessing, structure-guidance convolutional neural network predicting, converged convolutional neural network predicting and final output processing. According to the human body posture estimation method and system based on the structure guidance deep learning, the learning is guided by importing priori knowledge of a human body structure, and a human model is implicitly learned, so that human body postures can be accurately recognized, a certain complex postures and an occlusion problem can be accurately recognized, and high robustness is achieved.

Description

technical field [0001] The invention belongs to the field of computer vision and machine learning, and relates to a human body posture estimation method and system based on structure-guided deep learning. Background technique [0002] Human pose estimation is the process of locating the position of the joint points of the human body parts in the image. Human body pose estimation plays a very important role in the field of computer vision, and is the basis for applications such as human-computer interaction, virtual reality, and intelligent monitoring. Due to the diversity of people's clothing, shape, pose, background, etc. in the image, as well as some self-occlusion and other people or objects occlusion, it is very challenging to accurately predict the joint points of human body parts in the image. [0003] There are mainly two types of human pose estimation methods: traditional model-based methods and convolutional neural network-based methods that have recently emerged w...

AI Technical Summary

Problems solved by technology

Although this method uses deep learning technology, some complex postures, occlusions, and interference from multiple people cannot be resolved due to the lack of full use of the information of the human body itself.

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