Indoor human body pose detection method and device

A detection method and human body technology, applied in the field of indoor human body pose detection, can solve problems such as the inability to judge the pose of a human body or a living thing, and achieve object recommendation effect guarantee, objectivity and reliability guarantee, accuracy and reliability guarantee Effect

Pending Publication Date: 2022-02-25
苏州蓝赫朋勃智能科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The YOLO algorithm can only solve object detection, and cannot judge the pose of the human body or living things.

Method used

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  • Indoor human body pose detection method and device
  • Indoor human body pose detection method and device
  • Indoor human body pose detection method and device

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Embodiment Construction

[0045] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Apparently, the described embodiments are only some embodiments of the present disclosure, rather than all embodiments of the present invention, and it should be understood that the present invention is not limited by the exemplary embodiments described here.

[0046] It should be noted that the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0047]Those skilled in the art can understand that terms such as "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc. necessary logical sequence.

[0048] It should also be understood that in the embodiments of the present invention, "plurality" may...

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Abstract

The invention discloses an indoor human body pose detection method and device. The method comprises the following steps: distinguishing and judging various conditions obtained through a YOLO detection network model; when the M kinds of articles obtained through the YOLO detection network model have no area overlapping, judging according to the center coordinates of the human body and the center coordinates of the M kinds of articles; when certain areas of M articles obtained through the YOLO detection network model are overlapped, judging according to the Euclidean distance between the central coordinate of the human body and the central coordinate of the articles with the overlapped areas; and when the difference value of the Euclidean distance between the center coordinates of the human bodies and the center coordinates of the regional overlapped articles is within an error allowable range, judging the status according to a comfort coefficient. According to the embodiment of the invention, the accuracy of determining the human body pose of the user can be well ensured, so that actual requirements are met.

Description

technical field [0001] The invention relates to an indoor human body pose detection method and device, which are specifically applied to the indoor pose state judgment. Background technique [0002] With the development of big data, deep learning, and cloud computing, a method suitable for indoor target pose detection is constructed to judge the behavior status of indoor objects. Object pose detection and detection technologies are mainly divided into traditional object pose detection and object pose detection based on deep learning. The traditional object pose detection technology has the disadvantages of single object detection efficiency and low accuracy, while the object detection based on deep learning has the characteristics of multi-category detection and high detection accuracy. Joseph Redmon and others proposed the YOLO detection network model in 2015. The YOLO series is currently the most used one-stage target detection model, which can directly apply the YOLO alg...

Claims

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Application Information

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IPC IPC(8): G06T7/73G06T7/66G06V10/74G06K9/62G06N3/04G06N3/08
CPCG06T7/73G06T7/66G06N3/08G06T2207/30196G06N3/045G06F18/22
Inventor 王斌王英超张超王西志
Owner 苏州蓝赫朋勃智能科技有限公司
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