PEN: Pose-Embedding Network for Pedestrian Detection

doi:10.1109/TCSVT.2020.3000223

	PEN: Pose-Embedding Network for Pedestrian Detection
	Jiao, Yifan 1; Yao, Hantao2 ; Xu, Changsheng2,3
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN	1051-8215
	2021-03-01
卷号	31 期号:3 页码:1150-1162
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
摘要	In the past years, pedestrian detection has achieved significant progress via improving the visual description. However, the visual description is not robust to discover the occluded pedestrian, which is the bottleneck of the existing pedestrian methods. Targeting to overcome the shortcoming of visual description, we employ the human pose information, which is complementary to the visual description, to address the occlusion and false positive failure problems in pedestrian detection. The advantage of using human pose information is that the pose estimation model can localize the local part of the pedestrian once the pedestrian is occluded. By embedding the human pose information with the visual description, we propose a novel Pose-Embedding Network for pedestrian detection, which consists of two components: a Region Proposal Network, and a Pedestrian Recognization Network. The Region Proposal Network targets to generate lots of candidate proposals and corresponding confidence scores. Once obtaining the candidate proposals, the Pedestrian Recognization Network is proposed to distinguish pedestrian proposals by taking the visual information and pose information into consideration to refine the confidence scores and eliminate the false positives. Given the proposal image, the visual information is extracted with the Visual Feature Module. The Human Pose Module, which is proposed based on the pose estimation model, is used to predict the pose information. Further, the Classification Module is employed to fuse the visual and pose information and generates a pose-embedding pedestrian description. Extensive experiments on three challenging datasets, i.e., Caltech, CityPersons, and COCOPersons, show that the proposed approach achieves a significant improvement upon the state-of-the-art methods.
关键词	Visualization Proposals Detectors Feature extraction Object detection Pose estimation Fuses Pedestrian detection pedestrian recognization network pose-embedding pose information
DOI	10.1109/TCSVT.2020.3000223
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0102200] ; National Natural Science Foundation of China[61902399] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61720106006] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; National Postdoctoral Programme for Innovative Talents[BX20180358]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; National Postdoctoral Programme for Innovative Talents
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000626532100025
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	目标检测、跟踪与识别
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44191
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu, Changsheng
作者单位	1.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Jiao, Yifan,Yao, Hantao,Xu, Changsheng. PEN: Pose-Embedding Network for Pedestrian Detection[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2021,31(3):1150-1162.
APA	Jiao, Yifan,Yao, Hantao,&Xu, Changsheng.(2021).PEN: Pose-Embedding Network for Pedestrian Detection.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,31(3),1150-1162.
MLA	Jiao, Yifan,et al."PEN: Pose-Embedding Network for Pedestrian Detection".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 31.3(2021):1150-1162.