摘要
行人检测是计算机视觉领域的一个研究热点,针对目前算法中常采用非极大值抑制和硬阈值筛选的方法作为后处理,容易造成误检和漏检的问题,提出一种基于相似性度量的行人目标检测方法。首先,采用Faster RCNN生成一系列的目标候选集,应用非极大值抑制对候选集进行初步筛选,然后由较高置信度的目标区域建立特征模板,再根据特征相似性对较低置信度的目标区域做进一步判别,最后将筛选后的目标候选集和模板区域作为检测结果。在VOC、INRIA、Caltech数据集的实验结果证明,基于相似性度量的算法提高了行人检测的准确率。
Pedestrian detection has become a hot topic in the field of computer vision.Non-maximal suppression combined with hard threshold is the most common post-process method in pedestrian detection,whereas it is easy to cause false positive and false negative.As to this problem,this paper presents a pedestrian-object detection method based on similarity measurement.Firstly,Faster RCNN is used to build a series of candidate proposals among which initial selection is made based on non-maximal suppression.Then the authors create feature templates by target areas with high confidence,and make a further selection in the low-confidence proposals according to the feature similarity.Lastly,the detection results are composed of the reserved proposals and the templates.The experimental results from VOC,INRIA,Caltech datasets demonstrate that similarity measurement method can achieve higher pedestrian detection performance.
作者
李宗民
邢敏敏
刘玉杰
李华
LI Zongmin;XING Minmin;LIU Yujie;LI Hua(College of Computer and Communication Engineering,China University of Petroleum,Qingdao Shandong 266580,China;University of Chinese Academy of Sciences,Institute of Computing Technology Chinese Academy of Sciences,Beijing 100190,China)
出处
《图学学报》
CSCD
北大核心
2018年第5期901-908,共8页
Journal of Graphics
基金
国家自然科学基金项目(61379106
61379082
61227802)
山东省自然科学基金项目(ZR2013FM036
ZR2015FM011)
关键词
行人检测
目标候选集筛选
特征相似性度量
模板匹配
pedestrian detection
object proposals selecting
feature similarity measurement
template matching
