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 1351–1400 of 1488 papers

TitleStatusHype
Pedestrian Alignment Network for Large-scale Person Re-identificationCode0
Multiple People Tracking by Lifted Multicut and Person Re-Identification—0
Spindle Net: Person Re-Identification With Human Body Region Guided Feature Decomposition and FusionCode0
See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-Identification—0
Fast Person Re-Identification via Cross-Camera Semantic Binary Transformation—0
Consistent-Aware Deep Learning for Person Re-Identification in a Camera Network—0
One-Shot Metric Learning for Person Re-Identification—0
Point to Set Similarity Based Deep Feature Learning for Person Re-Identification—0
Hierarchical Gaussian Descriptors with Application to Person Re-Identification—0
Video-based Person Re-identification with Accumulative Motion ContextCode0
Transferring a Semantic Representation for Person Re-Identification and Search—0
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification—0
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks—0
Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification—0
Deep Mutual LearningCode0
Unsupervised Person Re-identification: Clustering and Fine-tuningCode0
Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-Identification—0
PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification—0
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification—0
Deep Person Re-Identification with Improved Embedding and Efficient TrainingCode0
Part-based Deep Hashing for Large-scale Person Re-identification—0
Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks—0
Enhancing Person Re-identification in a Self-trained SubspaceCode0
Quality Aware Network for Set to Set RecognitionCode0
Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification—0
Beyond triplet loss: a deep quadruplet network for person re-identificationCode0
Relevance Subject Machine: A Novel Person Re-identification Framework—0
Robust Depth-based Person Re-identification—0
Person Re-Identification by Camera Correlation Aware Feature Augmentation—0
Scalable Person Re-identification on Supervised Smoothed Manifold—0
In Defense of the Triplet Loss for Person Re-IdentificationCode0
Learning Correspondence Structures for Person Re-identification—0
SVDNet for Pedestrian Retrieval—0
Outlier Cluster Formation in Spectral Clustering—0
Learning Compact Appearance Representation for Video-based Person Re-Identification—0
Deep Hybrid Similarity Learning for Person Re-identification—0
Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification—0
Re-ranking Person Re-identification with k-reciprocal Encoding—0
Pose Invariant Embedding for Deep Person Re-identification—0
Person Re-Identification via Recurrent Feature AggregationCode0
Video-based Person Re-identification with Accumulative Motion Context—0
EgoReID: Cross-view Self-Identification and Human Re-identification in Egocentric and Surveillance Videos—0
Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints—0
Human-In-The-Loop Person Re-Identification—0
Highly Efficient Regression for Scalable Person Re-Identification—0
Deep Neural Networks with Inexact Matching for Person Re-IdentificationCode0
Person Re-Identification by Unsupervised Video Matching—0
Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification—0
Cross Domain Knowledge Transfer for Person Re-identification—0
A Discriminatively Learned CNN Embedding for Person Re-identificationCode0
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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