基于轻量级姿态估计的跳绳检测计数算法
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金应急管理项目(61741109)


Jump Rope Detection and Counting Algorithm Based on Lightweight Pose Estimation
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    针对人体姿态估计算法可实施性低以及基于姿态估计的跳绳计数精度不高的问题, 提出了一种基于轻量级人体姿态估计网络的跳绳计数算法. 该算法首先输入跳绳视频, 接着利用帧间差分法提取关键帧图像并送入人体姿态估计网络进行关节点检测; 同时为了解决轻量级网络检测精度不高的问题, 提出优化的LitePose检测模型, 采用自适应感知解码方法对模型的解码部分进行优化从而减少量化误差; 然后采用卡尔曼滤波对坐标数据进行平滑降噪, 以减小坐标抖动误差; 最终通过关键点坐标变化判断跳绳计数. 实验结果表明, 在相同图像分辨率和环境配置下, 本文提出的算法使用优化的LitePose-S网络模型, 不仅未增加模型参数量和运算复杂度, 同时网络检测精度提高了0.7%, 且优于其他对比网络, 而且本算法在跳绳计数时的平均误差率最低可达1.00%, 可以利用人体姿态估计的结果有效地判断人体起跳和落地情况, 最终得出计数结果.

    Abstract:

    To address the low feasibility of human pose estimation algorithms and low accuracy of jump rope counting based on pose estimation, this study proposes a jump rope counting algorithm based on a lightweight human pose estimation network. The algorithm first inputs a jump rope video, then extracts keyframe images by inter-frame difference method, and feeds them into the human pose estimation network for key joint point detection. To improve the detection accuracy of the lightweight network, the study builds an optimized LitePose detection model, which employs adaptive perception decoding to optimize the decoding part in the model and reduce quantization errors. Furthermore, a Kalman filter is adopted to smooth and denoise the coordinate data, reducing coordinate jitter errors. Finally, jump rope counting is determined based on the changes in key-point coordinates. Experimental results demonstrate that, in the same image resolution and environmental conditions, the proposed algorithm employing the optimized LitePose-S network model does not increase the parameter size and computational complexity of the model but improves network detection accuracy by 0.7% compared with other comparison networks. Meanwhile, the average error rate of this algorithm in jump rope counting can reach a minimum of 1.00%. The algorithm effectively determines the takeoff and landing of the human body by the results of human pose estimation and yields counting results.

    参考文献
    相似文献
    引证文献
引用本文

陈泽海,吴君钦,林俊宇.基于轻量级姿态估计的跳绳检测计数算法.计算机系统应用,2023,32(12):152-160

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-05-15
  • 最后修改日期:2023-06-14
  • 录用日期:
  • 在线发布日期: 2023-09-15
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号