Joint Person Objectness and Repulsion for Person Search

doi:10.1109/TIP.2020.3038347

	Joint Person Objectness and Repulsion for Person Search
	Yao, Hantao1 ; Xu, Changsheng1,2
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
	2021
卷号	30 页码:685-696
通讯作者	Xu, Changsheng(csxu@nlpria.ac.cn)
摘要	Person search targets to search the probe person from the unconstrainted scene images, which can be treated as the combination of person detection and person matching. However, the existing methods based on the Detection-Matching framework ignore the person objectness and repulsion (OR) which are both beneficial to reduce the effect of distractor images. In this paper, we propose an OR similarity by jointly considering the objectness and repulsion information. Besides the traditional visual similarity term, the OR similarity also contains an objectness term and a repulsion term. The objectness term can reduce the similarity of distractor images that not contain a person and boost the performance of person search by improving the ranking of positive samples. Because the probe person has a different person ID with its neighbors, the gallery images having a higher similarity with the neighbors of probe should have a lower similarity with the probe person. Based on this repulsion constraint, the repulsion term is proposed to reduce the similarity of distractor images that are not most similar to the probe person. Treating the Faster R-CNN as the person detector, the OR similarity is evaluated on PRW and CUHK-SYSU datasets by the Detection-Matching framework with six description models. The extensive experiments demonstrate that the proposed OR similarity can effectively reduce the similarity of distractor samples and further boost the performance of person search, e.g., improve the mAP from 92.32% to 93.23% for CUHK-SYSY dataset, and from 50.91% to 52.30% for PRW datasets.
关键词	Probes Search problems Detectors Proposals Visualization Noise measurement Transforms Detection-Matching person search person repulsion person objectness person re-identification
DOI	10.1109/TIP.2020.3038347
关键词[WOS]	NETWORK
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[U1836220] ; 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, Chinese Academy of Sciences (CAS)[QYZDJ-SSWJSC039]
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, Chinese Academy of Sciences (CAS)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000597161500006
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：28[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/173211/42684
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.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	模式识别国家重点实验室
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	Yao, Hantao,Xu, Changsheng. Joint Person Objectness and Repulsion for Person Search[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:685-696.
APA	Yao, Hantao,&Xu, Changsheng.(2021).Joint Person Objectness and Repulsion for Person Search.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,685-696.
MLA	Yao, Hantao,et al."Joint Person Objectness and Repulsion for Person Search".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):685-696.