摘要
鉴于目前散货码头运用智能视频监控系统时,由于不同方向人形的方向梯度直方图(Histogram of Oriented Gradient,HOG)特征存在较大的变化,使得用传统方法训练获得的少量特异性特征不足以支撑人形的有效分类,因此提出一种基于Ada Boost的针对不同姿势HOG特征的二级分类方法.首先将样本快速分为正(背)面人形和侧面人形,组成第一级分类;然后通过分别为两类样本训练子分类器组成第二级分类;第二级分类对人形进行识别,并对结果进行融合.以天津港干散货码头无人作业区为背景,完成一组人形识别实验.实验结果表明,相较于传统方法,该方法对正(背)面人形具有更高的识别率.二级分类方法整体上提高了人形识别的识别率.
At present,the intelligent video monitoring system is used in bulk terminals. However,Histo-gram of Oriented Gradient （HOG）features of human body in different directions show great difference, so that a small number of specific features obtained by the traditional method is insufficient to support ef-fective classification of human body. Therefore,a kind of two-stage classification method based on Ada-Boost is proposed for different HOG features of different human postures. Firstly,the samples are rapidly divided into the FrontBack （FB）human body and the side （not FB）human body to form the first-stage classification. Then,the sub-classifiers are respectively trained for the two types of samples to form the second-stage classification,where the human body and the non-human body are recognized respec-tively and the results are merged. Taking the unmanned area in the bulk terminal of Tianjin Port as thenbsp;background,a group of human body recognition experiments is carried out. The experimental results show that,compared with the traditional method,this method is of higher recognition rate for FB human body. The two-stage classification method improves the recognition rate of human body overall.
出处
《上海海事大学学报》
北大核心
2015年第3期82-86,102,共6页
Journal of Shanghai Maritime University
基金
上海市科学技术委员会部分地方院校能力建设专项计划(13510501800)
上海青年科技英才扬帆计划(15YF1404900)
上海市教育委员会科研创新项目(14ZZ140)
上海海事大学研究生创新基金(2014ycx040)
