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Human body hand skeleton detection method and system

A detection method and hand technology, applied in the field of image processing, can solve the problem of inaccurate detection of key points, and achieve the effect of high accuracy

Active Publication Date: 2021-12-24
FUZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Human hand skeleton estimation is more challenging in the scene of grasping, because the hand skeleton is occluded in this scene, making the detection of key points not accurate enough

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  • Human body hand skeleton detection method and system
  • Human body hand skeleton detection method and system

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily realize them. Also, for clarity, parts not related to describing the exemplary embodiments are omitted in the drawings.

[0043] In the present disclosure, it should be understood that terms such as "comprising" or "having" are intended to indicate the presence of features, numbers, steps, acts, components, parts or combinations thereof disclosed in the specification, and are not intended to exclude one or a plurality of other features, numbers, steps, acts, parts, parts or combinations thereof exist or are added.

[0044] In addition, it should be noted that, in the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be ...

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Abstract

The invention relates to a human body hand skeleton detection method, which comprises the following steps: firstly, processing a to-be-recognized picture, obtaining an initial posture of a target human body hand skeleton, then obtaining feature maps output by different decoding layers in the process of processing the to-be-recognized picture, and then processing the feature maps to obtain feature map data; extracting position data corresponding to an initial posture from the feature graph data as input data, and finally inputting the initial posture and the input data to a trained graph convolutional neural network to obtain a final posture of the target hand skeleton, wherein the matrix representation of the graph convolutional neural network is determined according to the constraint relation of the human hand skeleton structure. According to the technical scheme, the positions of the shielded key points can be accurately adjusted by combining the basic constraint information between the human body joint structures and the related data of the shielded key points contained in the mined feature map, so that the detection accuracy of the human body hand skeleton is relatively high.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method and system for detecting a human hand skeleton. Background technique [0002] Human hand skeleton estimation has long been the main content in the field of computational vision. Obtaining key points of the human body through skeleton analysis can simplify the process of motion estimation. Especially for some 3D human hand reconstruction tasks, hand skeleton estimation is a priori one of the tasks. In the prior art, hand skeleton detection is mainly divided into top-down and bottom-up methods. The top-down method first detects all hands in the scene, locates the detection frame of the hand, each detection frame contains the 2D key points of the hand skeleton, and then estimates the 3D hand through the fully connected layer of the 2D key points skeleton pose. The bottom-up approach is to detect the key points of the entire picture, and then group each key point...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06N3/045Y02D10/00
Inventor 林志贤林依林林珊玲林坚普张永爱周雄图叶芸郭太良
Owner FUZHOU UNIV
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