Gesture recognition system based on Euclidean skeleton

WANG Ping; HU Yan

doi:10.13873/J.1000-9787(2017)08-0111-03

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Gesture recognition system based on Euclidean skeleton

更新时间：2025-09-11

- Gesture recognition system based on Euclidean skeleton
- Vol. 36, Issue 8, Pages: 111-113+120(2017)
- 作者机构：
  
  天津大学电气与自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)08-0111-03
  CLC： TP391.41
- Published：2017
- 稿件说明：
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[1]王萍,胡炎.基于欧氏骨架的手势识别系统[J].传感器与微系统,2017,36(08):111-113+120.

WANG Ping, HU Yan. Gesture recognition system based on Euclidean skeleton[J]. 2017, 36(8): 111-113+120.
[1]王萍,胡炎.基于欧氏骨架的手势识别系统[J].传感器与微系统,2017,36(08):111-113+120. DOI： 10.13873/J.1000-9787(2017)08-0111-03.

WANG Ping, HU Yan. Gesture recognition system based on Euclidean skeleton[J]. 2017, 36(8): 111-113+120. DOI： 10.13873/J.1000-9787(2017)08-0111-03.

摘要

引进最新骨架提取算法

设计并实现了一种以手势的欧氏骨架为基准的手势识别系统

系统由通用视频采集模块和ARM开发板硬件组成。利用动态前景检测算法结合YCb Cr肤色识别模型

分割出手势区域;借助欧氏距离变换和Delta-中轴骨架提取算法获得手势区域的欧氏骨架

并提取骨架的关键点和欧氏距离等几何参数

以此建立手势识别的几何模型。实验测试正确识别率高达94%

每帧图片处理时间小于25 ms

表明该系统实时、有效。

Abstract

A gesture recognition system is developed based on the lasted skeleton extracting algorithm and Euclidean distance transform. The system consists of generic video acquisition module and ARM. Firstly

gestures region are segmented by dynamic forescene detection algorithm combined with skin color detection model of YCb Cr;secondly

the Delta-medial axis skeleton extraction algorithm and Euclidean distance transform are applied to obtain Eudidean skeleton so as to extract geometric parameters

and key points on the skeleton. Then a gesture recognition model is built by these geometric parameters. Test results show that the overall recognition rate reaches 94 %

processing time of each frame image is less than 25 ms

which shows that the system is real-time and effective.

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references

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