Joint Person Objectness and Repulsion for Person Search

doi:10.1109/TIP.2020.3038347

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Joint Person Objectness and Repulsion for Person Search
	Yao, Hantao 1; Xu, Changsheng 1,2
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2021
卷号	30 页码:685-696
关键词	Probes Search problems Detectors Proposals Visualization Noise measurement Transforms Detection-Matching person search person repulsion person objectness person re-identification
ISSN号	1057-7149
DOI	10.1109/TIP.2020.3038347
通讯作者	Xu, Changsheng(csxu@nlpria.ac.cn)
英文摘要	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.
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]
WOS关键词	NETWORK
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000597161500006
资助机构	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)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42683]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
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
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.