Person Re-Identification
508 papers with code • 33 benchmarks • 56 datasets
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.
Benchmarks
These leaderboards are used to track progress in Person Re-Identification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Person Re-Identification models and implementations
Market-1501
CUHK03
DukeMTMC-reID
MARS
VIPeR
CUHK-SYSU
SYSU-MM01
PRW
Occluded REID
Subtasks
-
Unsupervised Person Re-Identification
-
Video-Based Person Re-Identification
-
Generalizable Person Re-identification
-
Cloth-Changing Person Re-Identification
-
Cloth-Changing Person Re-Identification
-
Large-Scale Person Re-Identification
-
Cross-Modal Person Re-Identification
-
Self-Supervised Person Re-Identification
-
Clothes Changing Person Re-Identification
-
Image-To-Video Person Re-Identification
-
Semi-Supervised Person Re-Identification
-
Direct Transfer Person Re-identification
-
Federated Lifelong Person ReID
Latest papers with no code
Parameter Hierarchical Optimization for Visible-Infrared Person Re-Identification
Importantly, in the alignment process of SAS and AAL, all the parameters are immediately optimized with optimization principles rather than training the whole network, which yields a better parameter training manner.
Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment
Specifically, to address the first challenge, we propose a pseudo-label correction strategy that utilizes a Beta Mixture Model to predict the probability of mis-clustering based network's memory effect and rectifies the correspondence by adding a perceptual term to contrastive learning.
Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification
The memory dictionary-based contrastive learning method has achieved remarkable results in the field of unsupervised person Re-ID.
Part-Attention Based Model Make Occluded Person Re-Identification Stronger
However, occluded person ReID still suffers from background clutter and low-quality local feature representations, which limits model performance.
Diverse Representation Embedding for Lifelong Person Re-Identification
KU strategy enhances the adaptive learning ability of learner models for new information under the adjustment model prior, and KP strategy preserves old knowledge operated by representation-level alignment and logit-level supervision in limited old task datasets while guaranteeing the adaptive learning information capacity of the LReID model.
Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift
However, the uncertainty cannot be computed in the same way as classification in re-id since it is an open-set task, which does not share person labels between training and testing.
Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification
In particular, our method minimizes the cross-modal gap by identifying and aligning shared prototypes that capture key discriminative features across modalities, then uses multiple bridging steps based on this information to enhance the feature representation.
Lifelong Person Re-Identification with Backward-Compatibility
To this end, we devise the cross-model compatibility loss based on the contrastive learning with respect to the replay features across all the old datasets.
Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification
In addition, a Multi-scale Constraint Block (MCB) is designed, which extracts fine-grained identity-related features and effectively transfers cloth-irrelevant knowledge.
Enhancing Long-Term Person Re-Identification Using Global, Local Body Part, and Head Streams
The proposed framework consists of three streams: global, local body part, and head streams.
