Research on three frame difference gesture recognition method based on mixed bone features

Yongqiang ZHANG; Meilin SONG; Tianhu LIU; Menghua MAN

doi:10.11959/j.issn.2096-6652.202211

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Research on three frame difference gesture recognition method based on mixed bone features

Papers and Reports | 更新时间：2024-06-05

- Research on three frame difference gesture recognition method based on mixed bone features
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 4, Pages: 592-599(2022)
- 作者机构：
  
  1. 河北科技大学信息科学与工程学院，河北石家庄 050018
  2. 陆军工程大学石家庄校区电磁环境效应国家级重点实验室，河北石家庄 050003
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202211
  CLC： TP391
- Published Online：2022-12，
  
  Published：15 December 2022
- 稿件说明：
移动端阅览
Yongqiang ZHANG, Meilin SONG, Tianhu LIU, et al. Research on three frame difference gesture recognition method based on mixed bone features[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 592-599.
DOI：

Yongqiang ZHANG, Meilin SONG, Tianhu LIU, et al. Research on three frame difference gesture recognition method based on mixed bone features[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 592-599. DOI： 10.11959/j.issn.2096-6652.202211.

摘要

三帧间差分检测作为手势识别中的一种优秀算法，可在一定程度上解决二帧间差分法的“重影”问题。但三帧间差分检测在识别手势时会出现空洞，无法适应光照骤变等情况。针对此问题，提出了一种混合骨骼特征的三帧间差分手势识别方法。该方法首先对数据集进行二值化以统一大小，降低背景颜色干扰和计算量；然后使用混合骨骼特征的三帧间差分进行手势检测与追踪；最后通过神经网络进行手势识别分类。该方法可以有效地重点训练手部特征，显著降低了背景对手势识别的干扰，同时还提升了识别效率。经实验验证，该方法在复杂背景下的识别率最低为 93.38%，最高为 99.99%，满足鲁棒性的要求，为在复杂图像背景下的手势识别提供了一个新的思路。

Abstract

As an excellent algorithm in gesture recognition

three frame difference detection can solve the “double shadow” problem of two frame difference method to a certain extent.But the three frame difference detection will have holes when recognizing gestures

and it cannot adapt to sudden changes in lighting and so on.To solve this problem

the three frame difference gesture recognition method based on mixed bone features was proposed.Firstly

the binary size of the data set was unified

and the background color inter-ference and computation were reduced.Secondly

the three frame difference of mixed bone features was used to detect and track gestures.Finally

the neural network was used for gesture recognition and classification.This method could effectively train hand characteristics

significantly reduce the interference of background on gesture recognition

and improve the recognition efficiency.The experimental results showed that the minimum recognition rate of this method was 93.38% and the maximum was 99.99% in complex background

which could meet the requirement of robustness.This method provides a new idea for gesture recognition in complex image background.

关键词

Keywords

references

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