View source for Nec corporation (20240303855). POSTURE ESTIMATION APPARATUS, LEARNING MODEL GENERATION APPARATUS, POSTURE ESTIMATION METHOD, LEARNING MODEL GENERATION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM simplified abstract

=POSTURE ESTIMATION APPARATUS, LEARNING MODEL GENERATION APPARATUS, POSTURE ESTIMATION METHOD, LEARNING MODEL GENERATION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM=


==Organization Name==

[[:Category:nec corporation|nec corporation]]


[[Category:nec corporation]]

==Inventor(s)==

[[:Category:Yadong Pan of Tokyo (JP)|Yadong Pan of Tokyo (JP)]][[Category:Yadong Pan of Tokyo (JP)]]

==POSTURE ESTIMATION APPARATUS, LEARNING MODEL GENERATION APPARATUS, POSTURE ESTIMATION METHOD, LEARNING MODEL GENERATION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240303855 titled 'POSTURE ESTIMATION APPARATUS, LEARNING MODEL GENERATION APPARATUS, POSTURE ESTIMATION METHOD, LEARNING MODEL GENERATION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM

The posture estimation apparatus described in the abstract uses advanced technology to accurately estimate the posture of a person based on detected joint points in an image.

* Joint point detection unit identifies joint points of a person in an image.
* Reference point specifying unit assigns a preset reference point for each person.
* Attribution determination unit utilizes a learning model to establish the relationship between pixel data and unit vectors, calculating a score to determine the likelihood that a joint point belongs to a specific person.
* Posture estimation unit estimates the posture of the person based on the attribution determination results.

Potential Applications:
- Fitness and sports training applications
- Healthcare and physical therapy monitoring
- Virtual reality and gaming technology

Problems Solved:
- Accurate posture estimation from images
- Efficient identification of joint points in a person's body

Benefits:
- Improved accuracy in posture estimation
- Enhanced monitoring and analysis of physical movements
- Potential for personalized fitness and healthcare solutions

Commercial Applications:
Title: Advanced Posture Estimation Technology for Fitness and Healthcare Industries
This technology can be utilized in fitness tracking devices, healthcare monitoring systems, and virtual reality applications, offering precise posture estimation for various commercial purposes.

Questions about Posture Estimation Technology:
1. How does the attribution determination unit calculate the score indicating the possibility that a joint point belongs to a specific person?
- The attribution determination unit uses a learning model to analyze the relationship between pixel data and unit vectors, determining the likelihood that a joint point belongs to a particular person based on this analysis.

2. What are the key benefits of using this posture estimation technology in virtual reality applications?
- This technology can enhance the immersive experience in virtual reality by accurately tracking and estimating the posture of users, leading to more realistic interactions and movements in virtual environments.


==Original Abstract Submitted==

the posture estimation apparatus includes a joint point detection unit that detects joint points of a person in an image, a reference point specifying unit that specifies a preset reference point for each person, an attribution determination unit uses a learning model that machine-learns the relationship between a pixel data and the unit vector of the vector starting from a pixel to the reference point, to obtain a relationship between the detected joint points and the reference point of the each person in the image for each detected joint point, and to calculate a score indicating the possibility that the joint point belongs to the person, to determine the person in the image to which the joint point belongs by using the score, a posture estimation unit that estimates the posture of the person based on the result of determination by the attribution determination unit.



[[Category:G06T7/73]]
[[Category:CPC_G06T7/73]]