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题名: 基于前景分割的行人检测方法
其他题名: Human Detection Method Based on Foreground Segmentation
作者: 刘剑 ; 刘亚楠 ; 高恩阳 ; 龚志恒
作者部门: 装备制造技术研究室
关键词: 行人检测 ; HOG ; 前景分割 ; 闭合区域
刊名: 小型微型计算机系统
ISSN号: 1000-1220
出版日期: 2014
卷号: 35, 期号:3, 页码:654-658
收录类别: CSCD
产权排序: 3
摘要: 针对背景复杂情况下行人检测误检率较大的问题,提出一种新的基于前景分割的行人检测方法.本方法在样本训练过程中,通过对图像的初始轮廓线进行有向分水岭转换,然后由超度量轮廓图算法得到图像内一个个封闭的区域,把得到的封闭区域与设定框进行比较,区分封闭区域属于前景还是背景,进而把前景目标分割出来并进行训练;测试时,把待检测图像中的检测区域进行前景分割,求出前景的HOG特征并用SVM分类,确定检测区域内是否有行人.这样保证了在训练阶段和检测阶段都去除了背景噪声的影响,实验结果表明,提出的方法能有效的提高检测精度.
英文摘要: The complex background will greatly affect the test accuracy of human detection. In order to improve the accuracy of human detection,in this paper a new method of Foreground Segmentation has been proposed. This method is divided into two phases, in the sample training phase,through Oriented Watershed Transform and Ultrametric Contour Map,many closed regions in the image can be got,then we compare these closed regions with a box which has been set,and determine these closed regions is foreground or not. The foreground in the image can be got and trained. During the testing phase,the area in the test image which need to be detected can be segmentalized and the foreground can be got,then we can get the HOG of the foreground. By SVM,we know that there is a human in the area or not. So the foreground characteristic can be calculated which have no background noise in the sample training phase and testing phase. The experimental results show that this approach is effective in improving detection accuracy.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/15060
Appears in Collections:装备制造技术研究室_期刊论文

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