SOTAVerified

Person Re-Identification

Person Re-Identification is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.

Papers

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Showing 1201–1225 of 1488 papers

TitleStatusHype
Person Re-identification by Saliency Learning—0
Person Re-Identification by Semantic Region Representation and Topology Constraint—0
Person Re-Identification by Unsupervised Video Matching—0
Person Re-identification for Real-world Surveillance Systems—0
Person Re-identification in Appearance Impaired Scenarios—0
Person Re-Identification in Identity Regression Space—0
Person Re-identification in the Wild—0
Person Re-identification in Videos by Analyzing Spatio-Temporal Tubes—0
Person Re-identification Meets Image Search—0
Person Re-identification: Past, Present and Future—0
Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis—0
Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology—0
Person Re-Identification using Deep Learning Networks: A Systematic Review—0
Person Re-Identification Using Heterogeneous Local Graph Attention Networks—0
Person Re-identification Using Visual Attention—0
Person Re-Identification via Active Hard Sample Mining—0
Person re-identification via efficient inference in fully connected CRF—0
Person Re-Identification With Discriminatively Trained Viewpoint Invariant Dictionaries—0
Person Re-identification with Adversarial Triplet Embedding—0
Person Re-identification with Bias-controlled Adversarial Training—0
Person Re-Identification With Cascaded Pairwise Convolutions—0
Person Re-identification with Deep Similarity-Guided Graph Neural Network—0
Person re-identification with fusion of hand-crafted and deep pose-based body region features—0
Person Re-identification with Hyperspectral Multi-Camera Systems --- A Pilot Study—0
Person Re-identification with Metric Learning using Privileged Information—0
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All datasetsMarket-1501DukeMTMC-reIDMSMT17Occluded-DukeMTMCMarket-1501-CCUHK03 detectedCUHK03 labeledMARSCUHK03LTCCPRCCPRID2011

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1st-ReID(RE, RK)Rank-198—Unverified
2SSKD(GH)Rank-197.36—Unverified
3CLIP-ReID+Pose2ID (no RK)Rank-197.3—Unverified
4SOLIDER +UFFM+AMCRank-197—Unverified
5Unsupervised Pre-training (ResNet101+MGN)Rank-197—Unverified
6RGT&RGPR (RK)Rank-196.9—Unverified
7SOLIDERRank-196.9—Unverified
8LightMBN (RR)Rank-196.8—Unverified
9Viewpoint-Aware Loss(RK)Rank-196.79—Unverified
10SOLIDER (RK)Rank-196.7—Unverified
#ModelMetricClaimedVerifiedStatus
1DenseILmAP97.1—Unverified
2CTL Model (ResNet50, 256x128)mAP96.1—Unverified
3BPBreID (RK)mAP92.9—Unverified
4Unsupervised Pre-training (ResNet101+RK)mAP92.77—Unverified
5st-ReID(RE, RK,Cam)mAP92.7—Unverified
6RGT&RGPR (RK)mAP92.7—Unverified
7Viewpoint-Aware Loss(RK)mAP91.8—Unverified
8LDS (ResNet50 + RK)mAP91—Unverified
9Adaptive L2 Regularization (with re-ranking)mAP90.7—Unverified
10FlipReID (with re-ranking)mAP90.7—Unverified