Hierarchical Dance Pose Estimation Algorithm Based on Sequential Multi-Scale Feature Fusion

YANG Hong-hong; WANG Liu-li; ZHANG Yu-mei; WU Xiao-jun; DANG Yun-tong

doi:10.12263/DZXB.20200637

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Hierarchical Dance Pose Estimation Algorithm Based on Sequential Multi-Scale Feature Fusion

PAPERS | 更新时间：2025-12-08

- Hierarchical Dance Pose Estimation Algorithm Based on Sequential Multi-Scale Feature Fusion
- ACTA ELECTRONICA SINICA Vol. 49, Issue 12, Pages: 2428-2436(2021)
- 作者机构：
  
  1.陕西师范大学现代教学技术教育部重点实验室,陕西西安 710062
  2.陕西师范大学计算机科学学院,陕西西安 710062
  3.陕西师范大学音乐学院,陕西西安 710062
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200637
  CLC： TP391.4
- Received：29 June 2020，
  
  Revised：2021-07-27，
  
  Published：25 December 2021
- 稿件说明：
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杨红红,王刘丽,张玉梅等.基于序列多尺度特征融合表示的层级舞蹈动作姿态估计方法[J].电子学报,2021,49(12):2428-2436.

YANG Hong-hong,WANG Liu-li,ZHANG Yu-mei,et al.Hierarchical Dance Pose Estimation Algorithm Based on Sequential Multi-Scale Feature Fusion[J].ACTA ELECTRONICA SINICA,2021,49(12):2428-2436.
杨红红,王刘丽,张玉梅等.基于序列多尺度特征融合表示的层级舞蹈动作姿态估计方法[J].电子学报,2021,49(12):2428-2436. DOI： 10.12263/DZXB.20200637.

YANG Hong-hong,WANG Liu-li,ZHANG Yu-mei,et al.Hierarchical Dance Pose Estimation Algorithm Based on Sequential Multi-Scale Feature Fusion[J].ACTA ELECTRONICA SINICA,2021,49(12):2428-2436. DOI： 10.12263/DZXB.20200637.

摘要

人体姿态估计是计算机视觉研究领域的热点研究问题之一，但其在传统民间舞蹈动作姿态估计方面的应用研究尚处于起步阶段.由于舞蹈图像中人体动作复杂多变、舞蹈动作连贯性强、舞蹈者存在严重遮挡不易检测等特点，传统人体姿态估计方法难以准确估计舞蹈者的动作变化，导致舞蹈动作姿态估计准确率较低.针对此问题，本文提出一种基于序列多尺度特征融合表示的层级舞蹈动作姿态估计方法，该方法针对舞蹈动作骨骼关节点尺度变化剧烈的问题，构建基于序列多尺度特征融合表示的关节点估计模型.并且，针对舞蹈姿态形变较大，遮挡严重的问题，设计基于关节点几何关系的层级姿态估计模型，提高舞蹈动作姿态估计的效果.实验结果表明，本文方法在标准人体姿态估计数据集及自建舞蹈数据集上取得较好的姿态估计结果.

Abstract

Human pose estimation is one of the hot research topics in the field of computer vision

but its application in traditional dance pose estimation is still in its infancy. Due to the complexity of dance pose

the strong coherence of dance movements

and difficulty in detecting of dancers＇ poses caused by serious occlusion in dance images

the traditional human pose estimation methods are difficult to accurately estimate the pose changes of dancers

thus resulting in low accuracy in estimating dance pose. We propose a hierarchical dance pose estimation method based on sequential multi-scale feature fusion. To address the problems of the drastic scale changes of the dancer pose

a keypoint estimation model based on sequential multi-scale feature fusion is constructed. Furthermore

aiming to solve the issues that the large deformation and serious occlusion of dance pose

a hierarchical pose estimation model based on the geometric relationship between human keypoints is designed to improve the accuracy of dance pose estimation. The experimental results show that the proposed method can achieve good pose estimation results on the standard human pose estimation dataset and the self-collected dance dataset.

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references

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