Person Re-Identification
Person Re-Identification is a task in the field of computer vision that aims to match individual identities across different cameras or locations through video or image sequences. This task involves detecting and tracking people, and using features such as appearance, body shape, and clothing to efficiently and robustly identify the same person across multiple non-overlapping camera views. Its application value lies in improving the accuracy and efficiency of surveillance systems, intelligent security, and pedestrian retrieval.
Market-1501
LightMBN
DukeMTMC-reID
CTL Model (ResNet50, 256x128)
MSMT17
SOLIDER (with re-ranking)
Market-1501-C
CIL (ResNet-50)
CUHK03 labeled
PLR-OSNet
MARS
B-BOT + OSM + CL Centers* (Re-rank)
CUHK03
AlignedReID (RK)
CUHK03 detected
Top-DB-Net + RK
PRID2011
B-BOT + Attention and CL loss*
LTCC
AD-ViT
Occluded-DukeMTMC
BoT+UFFM+AMC
iLIDS-VID
GAF-Net
PRCC
CAL+GEFF
SYSU-30k
ResNet-50 (generalization)
CUHK03-C
CaceNet
eSports Sensors Dataset
MSMT17-C
SBS (ResNet-50)
Occluded REID
KPR + Pose2ID (no RK)
VC-Clothes
UAV-Human
DG-Net
CCVID
CAL+DLCR
CUHK-SYSU
AlignedReID
AG-ReID
VDT
CUHK03 (detected)
PAN+re-rank
DukeMTMC-VideoReID
PSTA
DukeTracklet
UTAL
IUST_PersonReID
CLIP-ReID
Market-1501->DukeMTMC-reID
OGNet
P-DukeMTMC-reID
BPBreID
Partial-REID
SYSU-MM01-C
AG-ReID.v2
V2E
ClonedPerson
DukeMTMC-reID->Market-1501
OGNet
ENTIRe-ID
TransReID (Strong Baseline)
Market-1501+500k
DLCE
Occluded-PoseTrack-ReID
RegDB
SenseReID
SoccerNet-v2
ViT-B/16
SYSU-MM01