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 1401–1425 of 1488 papers

TitleStatusHype
Densely Connected Convolutional NetworksCode1
A Siamese Long Short-Term Memory Architecture for Human Re-Identification—0
Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification—0
Temporal Model Adaptation for Person Re-Identification—0
Person Re-identification for Real-world Surveillance Systems—0
A Multi-task Deep Network for Person Re-identification—0
Person Re-identification with Hyperspectral Multi-Camera Systems --- A Pilot Study—0
Multi Channel-Kernel Canonical Correlation Analysis for Cross-View Person Re-Identification—0
Continuous Adaptation of Multi-Camera Person Identification Models through Sparse Non-redundant Representative Selection—0
End-to-End Comparative Attention Networks for Person Re-identification—0
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach—0
Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification—0
Improving Person Re-Identification via Pose-Aware Multi-Shot Matching—0
Similarity Learning With Spatial Constraints for Person Re-Identification—0
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification—0
Joint Learning of Single-Image and Cross-Image Representations for Person Re-Identification—0
Recurrent Convolutional Network for Video-Based Person Re-Identification—0
Joint Probabilistic Matching Using m-Best Solutions—0
Hierarchical Gaussian Descriptor for Person Re-Identification—0
Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles—0
Sample-Specific SVM Learning for Person Re-Identification—0
Person Re-Identification by Multi-Channel Parts-Based CNN With Improved Triplet Loss FunctionCode0
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsCode0
Generic Instance Search and Re-identification from One Example via Attributes and Categories—0
Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning—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
10TransReID-SSL (ViT-B w/o 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
5RGT&RGPR (RK)mAP92.7—Unverified
6st-ReID(RE, RK,Cam)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