[1]翟社平,李威,李炀. 人体特征检测算法的设计与实现[J].计算机技术与发展,2016,26(03):44-46.
　ZHAI She-ping,LI Wei,LI Yang. Design and Implementation of Human Characteristics Detection Algorithm[J].,2016,26(03):44-46.
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 人体特征检测算法的设计与实现()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年03期
页码:
44-46
栏目:
智能、算法、系统工程
出版日期:
2016-03-10

文章信息/Info

Title:
 Design and Implementation of Human Characteristics Detection Algorithm
文章编号:
1673-629X（2016)03-0044-03
作者:
 翟社平; 李威; 李炀
 西安邮电大学 计算机学院
Author(s):
 ZHAI She-ping; LI Wei; LI Yang
关键词:
 ; 现场可编程逻辑门阵列; 支持向量机; Adaboost ; 分类器
Keywords:
 ; FPGA; support vector machines; Adaboost; classifier
分类号:
TP391
文献标志码:
A
摘要:
 人体特征检测是特征识别技术的基础，特征检测技术运用到诸多领域，从刑侦领域的人脸检测到手机应用领域的指纹解锁都有涉及。为了实现人体特征检测算法的分析与研究，在现场可编程逻辑门阵列平台中，提出一种基于SAda-boost的人体特征检测算法。该算法结合了支持向量机和Adaboost分类器算法的优点，实现人体特征图像的降维处理，并对图像进行分类处理，实现人体特征元素的检测。整个设计基于ZedBoard硬件平台，该平台具有较强的可重构性和并行处理的能力，完成了人体特征图像检测算法的设计与实现，实现了对人体特征图像中的人脸、人眼、人体框架3种人体元素的检测。通过对比分析实验结果，验证了该算法的有效性。
Abstract:
 Human feature detection is the basis of feature recognition technology which has been applied to many areas,from the face of criminal investigation to fingerprint unlock in the field of mobile application is involved. In order to achieve the analysis and study of hu-man characteristics detection algorithm,in platform on field-programmable gate array,a body feature detection algorithm based on SAda-boost is presented. The algorithm combines the advantages of SVM and Adaboost classifier algorithm to reduce the dimension of human characteristics and to achieve the detection of human characteristic elements by image classification processing. Entire design is based on ZedBoard,which has a strong ability to reconfigurable and parallel processing,completing the design and implementation of human fea-tures image detection algorithm,realization of detection for human body elements,including face,eye,and human framework. Through comparative analysis of the experimental results,the validity of the algorithm is verified.

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更新日期/Last Update: 2016-06-08