<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Human pose estimation is a hot topic of computer research in recent years, which promotes the progress of society and brings many conveniences to people’s lives. Fom traditional methods to the mainstream deep learning-based methods, the primary approach in deep learning involves the use of convolutional neural networks to reduce computational complexity and improve network accuracy, but because the network structure is too deep to improve the accuracy, the trained model parameters are also very large, and it is very dependent on the input of hardware equipment. At this time, the lightweight human pose estimation can solve this problem very well. This paper mainly describes the knowledge of convolutional neural network in detail and compares it with traditional image algorithms. The OpenPose model is a classic model based on convolutional neural network that can well achieve single-person and multi-person human pose estimation model, but because the convolution kernel in its network structure is too large to increase the amount of calculation, this paper proposes three improvements to the network structure of the conventional OpenPose model. Finally, the precision of the model is improved by about 40%, which verifies the feasibility of lightweight human body posture estimation research.</p>
</abstract>

Human pose estimation is a hot topic of computer research in recent years, which promotes the progress of society and brings many conveniences to people’s lives. Fom traditional methods to the mainstream deep learning-based methods, the primary approach in deep learning involves the use of convolutional neural networks to reduce computational complexity and improve network accuracy, but because the network structure is too deep to improve the accuracy, the trained model parameters are also very large, and it is very dependent on the input of hardware equipment. At this time, the lightweight human pose estimation can solve this problem very well. This paper mainly describes the knowledge of convolutional neural network in detail and compares it with traditional image algorithms. The OpenPose model is a classic model based on convolutional neural network that can well achieve single-person and multi-person human pose estimation model, but because the convolution kernel in its network structure is too large to increase the amount of calculation, this paper proposes three improvements to the network structure of the conventional OpenPose model. Finally, the precision of the model is improved by about 40%, which verifies the feasibility of lightweight human body posture estimation research.

Research on Multi-Person Pose Estimation Technology

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on Multi-Person Pose Estimation Technology"}

Human pose estimation is a hot topic of computer research in recent years, which promotes the progress of society and brings many conveniences to people’s...

Average confidence of person i	1	2	3
OpenPose method	0.572	0.473	0.473
Lightweight OpenPose method	0.625	0.718	0.779

Model	AP, %
OpenPose	48.6
Optimization	86.3

Type	FPS
OpenPose	0.035
Lightweight OpenPose	20.889

	AP, %
MobileNet v1 (cut to conv4_1)	37.9
Dilated MobileNet v1 (cut to conv5_5)	42.8
Dilated MobileNet v1 (cut to conv5_6)	43.2
Dilated MobileNet v2 (cut to conv6_3)	39.6

Research on Multi-Person Pose Estimation Technology

Publicado en línea: 16 mar 2024

Páginas: 38 - 44

DOI: https://doi.org/10.2478/ijanmc-2023-0075

Palabras clave
Convolutional Neural Network, Network Structure, OpenPose Model, Multi-person Human Pose Estimation

© 2023 Hongyan Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Comparison of Average Confidence

Improving OpenPose network evaluation results

Comparison before and after network structure improvement

Lightweight backbone performance comparison

Research on Multi-Person Pose Estimation Technology

Publicado en línea: 16 mar 2024

Páginas: 38 - 44

DOI: https://doi.org/10.2478/ijanmc-2023-0075

Palabras claveConvolutional Neural Network, Network Structure, OpenPose Model, Multi-person Human Pose Estimation

© 2023 Hongyan Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Comparison of Average Confidence

Improving OpenPose network evaluation results

Comparison before and after network structure improvement

Lightweight backbone performance comparison

Palabras clave
Convolutional Neural Network, Network Structure, OpenPose Model, Multi-person Human Pose Estimation