Multi‐granularity re‐ranking for visible‐infrared person re‐identification  

作　　者：Yadi Wang Hongyun Zhang Duoqian Miao Witold Pedrycz 

机构地区：[1]Department of Computer Science and Technology,Tongji University,Shanghai,China [2]Key Laboratory of Embedded System and Service Computing,Ministry of Education,Shanghai,China [3]Department of Electrical and Computer Engineering,University of Alberta,Edmonton,Alberta,Canada [4]System Research Institute,Polish Academy of Sciences,Warsaw,Poland

出　　处：《CAAI Transactions on Intelligence Technology》2023年第3期770-779,共10页智能技术学报（英文）

基　　金：supported by the National Natural Science Foundation of China(Serial No.61976158 and No.62076182),and the Jiangxi“Double Thousand Plan”.

摘　　要：Visible‐infrared person re‐identification(VI‐ReID)is a supplementary task of single‐modality re‐identification,which makes up for the defect of conventional re‐identification under insufficient illumination.It is more challenging than single‐modality ReID because,in addition to difficulties in pedestrian posture,camera shoot-ing angle and background change,there are also difficulties in the cross‐modality gap.Existing works only involve coarse‐grained global features in the re‐ranking calculation,which cannot effectively use fine‐grained features.However,fine‐grained features are particularly important due to the lack of information in cross‐modality re‐ID.To this end,the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm(termed QCMR)is proposed,including a Q‐nearest neighbour centre encoder(termed QNC)and a Multi‐granularity K‐reciprocal Encoder(termed MGK)for a more comprehensive feature representation.QNC converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality gap.MGK takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity measurement.Extensive experiments on two widely used VI‐ReID benchmarks,SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art results.Especially,the mAP of SYSU‐MM01 is increased by 5.9%in all‐search mode.

关 键 词：computer vision recognition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]