Lightweight human body posture estimation method and system based on streaming attention

A technology for human body posture and posture estimation, applied in neural learning methods, calculations, computer components and other directions, can solve the problems of high complexity and low recognition accuracy, and achieve the effect of low complexity, high recognition accuracy and high precision

Pending Publication Date: 2022-05-24
HUNAN UNIV
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AI Technical Summary

Problems solved by technology

[0005] The present invention provides a lightweight human body pose estimation method and system based on streaming attention, which is used to solve the technical problems of low recognition accuracy and high complexity of the existing human body pose estimation network

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  • Lightweight human body posture estimation method and system based on streaming attention
  • Lightweight human body posture estimation method and system based on streaming attention
  • Lightweight human body posture estimation method and system based on streaming attention

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Embodiment Construction

[0037] Embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0038] see figure 1 , the lightweight body pose estimation method based on streaming attention of the present invention includes the following steps:

[0039] Obtain the target image that needs attitude estimation (the target image that needs attitude estimation can be obtained through a device such as a mobile phone camera;); standardize the cropping and normalization of the target image; input the target image into the trained stream attention-based light source. In the magnitude human pose estimation network, output pose estimation heat map results;

[0040] Among them, the trained flow-based attention based lightweight human pose estimation network (Flow-based Attention Lightweight Network, FALNet) is obtained through the following steps:

[0041] ...

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Abstract

The invention discloses a lightweight human body posture estimation method and system based on streaming attention. The method comprises the steps that a target image needing posture estimation is acquired; carrying out standardized clipping and normalization processing on the target image; inputting the target image into a trained lightweight human body posture estimation network based on streaming attention, and outputting a posture estimation heat map result; a model training step: performing down-sampling on training images in the training data set through convolution of different scales, obtaining four first feature maps of different scales as input of an up-sampling module through multiple groups of ghost lightweight modules with streaming attention, and performing convolution respectively to obtain four second feature maps of the same number of channels; and carrying out bilinear up-sampling operation to obtain a double feature map, carrying out feature fusion, outputting a third feature map, and converting to obtain a heat map required by human body posture estimation. The method is high in recognition precision and low in complexity.

Description

technical field [0001] The present invention relates to the field of human body posture estimation, in particular to a light-weight human body posture estimation method and system based on streaming attention. Background technique [0002] In the direction of computer vision, human pose estimation is a very hot and important topic. Its main task is to obtain human joint points and limb information based on images or videos. After obtaining this information, some cognitive tasks can be further completed. Such as behavior recognition and human-computer interaction. [0003] There are generally two ideas for human pose estimation tasks: top-down and bottom-up, so the structure design of the neural network is also of these two ideas. The top-down human pose estimation algorithm consists of two steps: object detection and single-person pose estimation. That is, first use the classic target detection algorithm such as Faster-RCNN to frame the people in the image, and then perfo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V40/20G06V10/20G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 肖德贵刘家辉
Owner HUNAN UNIV
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