[1]杨忠明,李子龙,胡音文,等.一种前景提取的行人模式识别检测算法[J].郑州大学学报(工学版),2019,40(05):91-96.
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一种前景提取的行人模式识别检测算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年05期
页码:
91-96
栏目:
出版日期:
2019-09-20

文章信息/Info

作者:
杨忠明李子龙胡音文黄翰蔡昭权
广东科学技术职业学院计算机工程技术学院,广东珠海;华南理工大学软件学院,广东 广州;惠州学院,广东惠州
关键词:
背景建模行人检测人脸检测AdaBoost模式识别
Keywords:
background modeling pedestrian detection face detection AdaBoost model recognition
文献标志码:
A
摘要:
通过深入研究高斯混合模型.H0G+SVM分类器和Haar+AdaBoost分类器的算法原理,提出了 一种基于前景提取和模式识别的行人检测算法,首先使用高斯混合模型进行背景建模,通过前景提取的 方法提取出运动物体,对运动物体进行行人检测,最后对判断为行人的对象进行人脸检测,分类区分出 蒙面的可疑行人,解决基于背景建模方法的误判问题和基于统计学习方法的效率问题.实验结果表明, 相对于单独使用模式识别算法,新算法显著降低了漏检率,且在运行速度和检测率方面表现良好.
Abstract:
In this paper, the algorithm principles of Gaussian mixture model, HOG+SVM classifier and Haar+ Adaboost classifier were exploved. A pedestrian detection algorithm based on foreground extraction and pattern recognition was proposed. The background modelling was executed by using Gaussian mixture model and then the moving object was entracted by using foreground modeling methods. The pedestrian detection hased on the moving objects and face recognition on the objects were execnted. By this, the misjudgment problems was solved based on background modeling methods and efficiency problems based on statistical learning methods. The experimental results showed that the new algorithm could greatly reduce the missed detection rate compared to using the pattern recognition algorithm alone, and it performed well in terms of running speed and detection rate.
更新日期/Last Update: 2019-10-26