复杂环境下通用的手势识别方法

doi:10.11772/j.issn.1001-9081.2016.07.1965

计算机应用 ›› 2016, Vol. 36 ›› Issue (7): 1965-1970.DOI: 10.11772/j.issn.1001-9081.2016.07.1965

复杂环境下通用的手势识别方法

杜堃, 谭台哲

广东工业大学计算机学院, 广州 510006

收稿日期:2015-12-14 修回日期:2016-03-15 出版日期:2016-07-10 发布日期:2016-07-14
通讯作者: 杜堃
作者简介:杜堃(1990-),男,湖北宜昌人,硕士研究生,主要研究方向:模式识别、图像处理;谭台哲(1970-),男,山东莱阳人,副教授,博士,主要研究方向:计算机视觉处理、生物特征识别。

General method for gesture recognition in complex environment

DU Kun, TAN Taizhe

School of Computer Science and Technology, Guangdong University of Technology, Guangzhou Guangdong 511006, China

Received:2015-12-14 Revised:2016-03-15 Online:2016-07-10 Published:2016-07-14

摘要/Abstract

摘要： 用来解决手势识别中光照变化、背景干扰等问题的方法，往往计算量大，耗时长。针对这一问题，提出了一种复杂环境下通用的手势识别方法。该方法利用二进制方式实现支持向量机（SVM）模型并且使用位运算代替滑动窗口从而完成目标快速筛选，然后用统一计算设备架构（CUDA）实现卷积神经网络对初筛区域进行二次判断和识别。该方法不依赖于动态手势识别技术，可以应用于动态和静态的手势识别，能够同时处理光照变化、背景干扰的问题。实验结果表明所提算法的计算效率相比基于滑动窗口的算法有100至1000倍的提升，处理一幅图片的时间约为0.01 s。在修正后的Marcel数据集上实验结果达到了96.1%的准确率和100%的召回率。效率上的提升使得算法能够实时进行复杂环境下的手势识别。

关键词: 手势识别, 位运算, 卷积神经网络, 复杂环境, 肤色似然

Abstract: The methods for dealing with influence of light and complex background often consume large calculation and long time. To solve this problem, a general method of gesture recognition in complex environment was proposed. The proposed method was based on the binary Support Vector Machine (SVM) and bitwise operation instead of sliding window to achieve the goal of rapid screening, and then Compute Unified Device Architecture (CUDA) was used to build a convolutional neural network to re-judge the initial screen area. The proposed method does not rely on dynamic gesture recognition techniques, and can be used for both dynamic and static gesture recognition. The method can deal with the problem of illumination change and background interference. The experimental results show that compared with the methods based on sliding window, the computational efficiency is improved by 100 to 1000 times. It takes less than 0.01 s to process a picture. The experimental results on the modified Marcel data set show that its precision achieves 96.1% and recall achieves 100%. The proposed algorithm can be used for real-time hand gesture recognition under complex environment for its high performance.

Key words: gesture recognition, bitwise operation, convolutional neural network, complex environment, skin like-hood

中图分类号:

TP391.413

杜堃, 谭台哲. 复杂环境下通用的手势识别方法[J]. 计算机应用, 2016, 36(7): 1965-1970.

DU Kun, TAN Taizhe. General method for gesture recognition in complex environment[J]. Journal of Computer Applications, 2016, 36(7): 1965-1970.

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复杂环境下通用的手势识别方法

General method for gesture recognition in complex environment

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