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

A human body posture and deep learning technology, applied in the fields of computer vision and machine learning, can solve the problems of not making full use of the human body and occlusion and interference, and achieve the effect of improving the prediction accuracy

Inactive Publication Date: 2017-05-10
NANJING UNIV
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Problems solved by technology

Although this method uses deep learning technology, some complex postures, occlusions, and interference from

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

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Embodiment Construction

[0042] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: It should be understood that the preferred embodiments are only for illustrating the present invention, rather than limiting the protection scope of the present invention.

[0043] like figure 1 Shown, the present invention is based on the human body pose estimation method of structure instruction deep learning, comprises the following steps:

[0044] Step 1: Input data preprocessing.

[0045]First, the picture is scaled so that the length of the longer side is a fixed value, preferably 256; secondly, the shorter side is filled with zeros to make the whole picture a square; then, random left and right flips are performed; then, press a certain value Rotate the picture at an angle of , preferably, every 10 degrees, up to a maximum of 180 degrees. At the same time as the above-mentioned operations on the picture, it is necessary to perform corr...

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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...

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Application Information

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IPC IPC(8): G06K9/62G06K9/00
CPCG06V40/23G06V40/103G06F18/25
Inventor 周余艾宝乐于耀
Owner NANJING UNIV
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