Research of dynamic gesture recognition based on multi-instance learning of kinematics features

Cai-qiu ZHOU; Yu-wang YANG; Hai-bo PANG

doi:10.11959/j.issn.1000-436x.2017211

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Research of dynamic gesture recognition based on multi-instance learning of kinematics features

Papers | 更新时间：2024-06-05

- Research of dynamic gesture recognition based on multi-instance learning of kinematics features
- Journal on Communications Vol. 38, Issue 11, Pages: 103-110(2017)
- 作者机构：
  
  1. 南京理工大学计算机科学与工程学院，江苏南京 210094
  2. 郑州大学软件与应用科技学院，河南郑州 450002
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61640020);The National Natural Science Foundation of China(61402420);Higher Education Institutions Program of Henan Province(17A520014);Scientific and Technological Research Project in Henan Province(172102310496)
- DOI：10.11959/j.issn.1000-436x.2017211
  CLC： TP391
- Online First：2017-11，
  
  Published：25 November 2017
- 稿件说明：
移动端阅览
Cai-qiu ZHOU, Yu-wang YANG, Hai-bo PANG. Research of dynamic gesture recognition based on multi-instance learning of kinematics features[J]. Journal on Communications, 2017, 38(11): 103-110.
DOI：

Cai-qiu ZHOU, Yu-wang YANG, Hai-bo PANG. Research of dynamic gesture recognition based on multi-instance learning of kinematics features[J]. Journal on Communications, 2017, 38(11): 103-110. DOI： 10.11959/j.issn.1000-436x.2017211.

摘要

在动态手势特征提取和识别方面，利用运动学模式解决动态手势识别问题，在光流场基础上计算出散度模式，旋度模式，对称模式，反对称模式，梯度张量第二、第三主不变模式，应变张量第二、第三主不变模式以及自旋转张量第三主不变模式；进一步提出一种基于多实例学习的方法，将每一个动态手势的所有运动主模式构成一个动态手势词袋，将未知类型动态手势的运动主模式与词袋空间中对应运动主模式进行相似度计算，利用最近邻方法对手势进行识别。实验结果表明：基于多实例运动学主模式学习的动态手势识别方法取得了较高的识别率。

Abstract

Compared to static gestures

dynamic gestures had some new characteristics.The problems of dynamic gestures recognition was spewed by using kinematics mode

such as divergence modes

curl modes

symmetric and ant-symmetric modes

the second and third principal invariant modes of the gradient tensor

the second and third principal invariant modes of the strain tensor and the third principal invariant modes of spin tensor; Further

a framework based on multi-instance learning was proposed

organize all these principle modes for each gesture were organized to a dynamic gestures bag-of-words

and the similarity between the mode of unknown type dynamic gestures and the all bag-of-words were calculated.Then

the nearest neighbor method was used to recognize the dynamic gestures.The experimental results show that the dynamic gestures recognition based on multi-instance kinematics features principal mode learning methods can obtain a higher recognition rate.

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references

周航 . 基于计算机视觉的手势识别系统研究 [D ] . 北京:北京交通大学 , 2008 .

ZHOU H . Research on gesture recognition system based on computer vision [D ] . Beijing:Beijing Jiaotong University , 2008 .

顾立忠 . 基于表观的手势识别及人机交互技术研究 [D ] . 上海:上海交通大学 , 2008 .

GU L Z . Research on surface based hand gesture recognition and human-computer interaction technology [D ] . Shanghai:Shanghai Jiaotong University , 2008 .

KHAN R , HANBURY A , THINGER J et al . Color based skin classification [J ] . Pattern Recognition Letters , 2012 , 33 ( 2 ): 157 - 163 .

SUN H M . Skin detection for single images using dynamic skin color modeling [J ] . Pattern Recognition , 2010 , 43 ( 4 ): 1413 - 1420 .

MOLINA L , ESCUDERO-Viñolo M , SIGNORIELLO A , et al . Real-time user independent hand gesture recognition from time-offlight camera video using static and dynamic models [J ] . Machine Vision and Applications , 2013 , 24 ( 1 ): 187 - 204 .

FAN L , . A feature-based object tracking method using online template switching and feature adaptation [C ] // The 6th International Conference on Image and Graphics . 2011 : 707 - 713 .

LIU Y , ZHOUW , YIN H G , et al . Tracking based on SURF and superpixel [C ] // The 6th International Conference on Image and Graphics . 2011 : 714 - 719 .

WANG W , TUNG C L . Dynamic hand gesture recognition using hierarchical dynamic Bayesian networks through low-level image processing [C ] // The 7th International Conference on Machine Learning and Cybernetics . 2008 : 3247 - 3253 .

SGOUROPOULOS K , STERGIOPOULOU E , PAPAMARKOS N . A dynamic gesture and posture recognition [J ] . Journal of Intelligent ＆Robotic Systems , 2014 , 76 ( 2 ): 283 - 296 .

DURBIN P A , REIF B . Statistical Theory and Modeling for Turbulent Flows [M ] . Second edition.John Wiley ＆ Sons,Ltd.,Chichester . 2011 .

ALI S , SHAH M . Human action recognition in videos using kinematic features and multiple instance learning [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2008 , 32 ( 2 ): 288 - 303 .

庞海波 , 李占波 , 丁友东 . 基于时间序列手势轮廓模型的动态手势识别 [J ] . 华南理工大学学报(自然科学版) , 2015 , 43 ( 1 ): 140 - 146 .

PANG H B , LI Z B , DING Y D . Dynamic hand gesture recognition based on time series gesture contour model [J ] . Journal of South China University of Technology (Natural Science Edition) , 2015 , 43 ( 1 ): 140 - 146 .

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