基于前景分割的行人检测方法  

Human Detection Method Based on Foreground Segmentation

作　　者：刘剑[1,2] 刘亚楠[1] 高恩阳[1,3] 龚志恒[1] 

机构地区：[1]沈阳建筑大学信息与控制工程学院,沈阳110168 [2]东北大学信息科学与工程学院,沈阳110004 [3]中国科学院沈阳自动化所,沈阳110016

出　　处：《小型微型计算机系统》2014年第3期654-658,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61070024)资助;辽宁省住建部(2010-K9-22)资助

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.The complex background will greatly affect the test accuracy of human detection. In order to improve the accuracy of hu- man 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.

关 键 词：行人检测 HOG 前景分割 闭合区域 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]