View source for 18275791. LEARNING APPARATUS, ESTIMATION APPARATUS, LEARNING METHOD, ESTIMATION METHOD, AND PROGRAM AND NON-TRANSITORY STORAGE MEDIUM simplified abstract (NEC Corporation)

=LEARNING APPARATUS, ESTIMATION APPARATUS, LEARNING METHOD, ESTIMATION METHOD, AND PROGRAM AND NON-TRANSITORY STORAGE MEDIUM=


==Organization Name==

[[:Category:NEC Corporation|NEC Corporation]]


[[Category:NEC Corporation]]

==Inventor(s)==

[[:Category:Hiroo Ikeda of Tokyo (JP)|Hiroo Ikeda of Tokyo (JP)]][[Category:Hiroo Ikeda of Tokyo (JP)]]

==LEARNING APPARATUS, ESTIMATION APPARATUS, LEARNING METHOD, ESTIMATION METHOD, AND PROGRAM AND NON-TRANSITORY STORAGE MEDIUM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18275791 titled 'LEARNING APPARATUS, ESTIMATION APPARATUS, LEARNING METHOD, ESTIMATION METHOD, AND PROGRAM AND NON-TRANSITORY STORAGE MEDIUM

==Simplified Explanation==

The present invention provides a learning apparatus including an acquisition unit that acquires learning data associating a training image including a person with correct answer labels indicating the position of each person, visibility of keypoints on the body, and the position of visible keypoints within the image. The learning unit then learns an estimation model based on this data to estimate information related to the position of each person, visibility of keypoints, and the position of visible keypoints within a processing image.

* Acquisition unit acquires learning data associating a training image with correct answer labels for person position, keypoint visibility, and keypoint position within the image.
* Learning unit learns an estimation model based on the learning data to estimate information related to person position, keypoint visibility, and keypoint position within a processing image.

== Potential Applications ==
This technology could be applied in various fields such as computer vision, image processing, and artificial intelligence for tasks like human pose estimation, action recognition, and gesture recognition.

== Problems Solved ==
This technology solves the problem of accurately estimating the position of people and keypoints within images, which is crucial for tasks like human activity recognition and human-computer interaction.

== Benefits ==
The benefits of this technology include improved accuracy in estimating person positions and keypoints within images, leading to better performance in applications like surveillance, sports analysis, and healthcare monitoring.

== Potential Commercial Applications ==
A potential commercial application of this technology could be in developing advanced surveillance systems that can accurately track and analyze human movements in real-time.

== Possible Prior Art ==
One possible prior art for this technology could be existing human pose estimation algorithms that use deep learning models to estimate keypoint positions in images.

=== Unanswered Questions ===

=== How does this technology compare to existing human pose estimation methods in terms of accuracy and efficiency? ===
This article does not provide a direct comparison with existing human pose estimation methods, so it is unclear how this technology performs in comparison to others.

=== What are the limitations of this technology in real-world applications, and how can they be addressed? ===
The article does not discuss the limitations of this technology or potential challenges in real-world applications, leaving room for further exploration into these areas.


==Original Abstract Submitted==

The present invention provides a learning apparatus () including an acquisition unit () that acquires learning data associating a training image including a person with a correct answer label indicating a position of each person, a correct answer label indicating whether each of a plurality of keypoints of a body of each of the persons is visible in the training image, and a correct answer label indicating a position, within the training image, of the keypoint being visible in the training image among a plurality of the keypoints, and a learning unit () that learns, based on the learning data, an estimation model that estimates information indicating a position of each person, information indicating whether each of a plurality of the keypoints of each person included in a processing image is visible in the processing image, and information being related to a position of each of keypoints for computing a position, within the processing image, of the keypoint being visible in the processing image.



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