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Attitude estimation method based on decoupling step network

A pose estimation and decoupling technology, applied in neural learning methods, biological neural network models, calculations, etc., can solve problems such as reducing the accuracy of human pose estimation, improve training and reasoning speed, facilitate fusion, and reduce the amount of parameters Effect

Active Publication Date: 2021-02-26
HUAQIAO UNIVERSITY +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method ensures the diversity of image features, improves the recognition accuracy and speeds up the network operation speed. For the field of human body pose estimation, although the network operation speed is improved, the accuracy of human body pose estimation is reduced.

Method used

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  • Attitude estimation method based on decoupling step network
  • Attitude estimation method based on decoupling step network
  • Attitude estimation method based on decoupling step network

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Embodiment Construction

[0057] The general idea of ​​the technical solution in the embodiment of the application is as follows:

[0058] First of all, the decoupling ladder network is built based on the decoupling residual module, which greatly reduces the parameter amount of the deep convolutional neural network, and the reduced accuracy is within an acceptable range; secondly, each decoupling residual in the decoupling ladder network The flow of information between groups enables the decoupled ladder network to effectively utilize and fuse spatial information and semantic information, making the pose estimation results more accurate; then, add waterfall to every two decoupled residual modules of the decoupled ladder network The module effectively makes up for the lack of receptive field caused by the decoupling residual module, which makes the accuracy of each joint point of the human body more balanced, greatly improves the accuracy of attitude estimation, and provides an attitude reference for beh...

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Abstract

The invention provides an attitude estimation method based on a decoupling step network in the technical field of human body attitude estimation. The attitude estimation method comprises the followingsteps of S10, building the decoupling step network based on a decoupling residual module and a waterfall module; S20, acquiring a large number of human body sample images, and training a decoupling step network by using the human body sample images; and S30, inputting a to-be-tested image into the trained decoupling step network, calculating the position of each joint point in the to-be-tested image, and forming a complete human body posture based on the position of each joint point. The method has the advantage that the speed and precision of human body posture estimation are greatly improved.

Description

technical field [0001] The present invention relates to the technical field of human body pose estimation, in particular to a pose estimation method based on a decoupling ladder network. Background technique [0002] Human body posture estimation is a key step for computer vision to further understand human behavior. Through an RGB image, all relevant nodes of the human body can be effectively predicted and the correct posture can be formed. Accurately predicting human body posture is essential for higher-level computer vision tasks, such as human It is of great significance for behavior recognition, human-computer interaction, pedestrian re-identification, and abnormal behavior detection. [0003] Although the field of human pose estimation is developing rapidly, both top-down and bottom-up methods have the problem of complex network structure and large number of parameters, which makes it difficult to know which part is more critical when training the network, which leads ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/04G06N3/08G06T5/30G06T7/62
CPCG06N3/08G06T5/30G06T7/62G06T2207/10004G06T2207/30196G06T2207/20081G06T2207/20084G06V40/20G06V10/25G06N3/045G06F18/241
Inventor 骆炎民欧志龙林躬耕
Owner HUAQIAO UNIVERSITY
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