Person Re-Identification
511 papers with code • 34 benchmarks • 57 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
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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
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Unsupervised Person Re-Identification
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Video-Based Person Re-Identification
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Generalizable Person Re-identification
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Cloth-Changing Person Re-Identification
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Cloth-Changing Person Re-Identification
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Large-Scale Person Re-Identification
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Cross-Modal Person Re-Identification
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Self-Supervised Person Re-Identification
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Clothes Changing Person Re-Identification
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Image-To-Video Person Re-Identification
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Semi-Supervised Person Re-Identification
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Direct Transfer Person Re-identification
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Federated Lifelong Person ReID
Latest papers
DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification
Unlike mainstream approaches using global features for simultaneous multi-task learning of ReID and human parsing, or relying on semantic information for attention guidance, DROP argues that the inferior performance of the former is due to distinct granularity requirements for ReID and human parsing features.
A Survey on 3D Skeleton Based Person Re-Identification: Approaches, Designs, Challenges, and Future Directions
Person re-identification via 3D skeletons is an important emerging research area that triggers great interest in the pattern recognition community.
Image-based human re-identification: Which covariates are actually (the most) important?
KaiyangZhou/deep-person-reid
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Image and Vision Computing 2024
Human re-identification (re-ID) is nowadays among the most popular topics in computer vision, due to the increasing importance given to safety/security in modern societies.
Exploring Color Invariance through Image-Level Ensemble Learning
This issue is particularly pronounced in complex wide-area surveillance scenarios, such as person re-identification and industrial dust segmentation, where models often experience a decline in performance due to overfitting on color information during training, given the presence of environmental variations.
CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Re-Identification
Weakly supervised text-based person re-identification (TPRe-ID) seeks to retrieve images of a target person using textual descriptions, without relying on identity annotations and is more challenging and practical.
Mutual Distillation Learning For Person Re-Identification
kuilongcui/mdpr
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With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements.
Masked Attribute Description Embedding for Cloth-Changing Person Re-identification
moon-wh/made
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To address this, we mask the clothing and color information in the personal attribute description extracted through an attribute detection model.
AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification
To address this, we introduce AG-ReID. v2, a dataset specifically designed for person Re-ID in mixed aerial and ground scenarios.
Temporal 3D Shape Modeling for Video-Based Cloth-Changing Person Re-Identification
dustin-nguyen-qil/Videobased-ClothChanging-ReID-Baseline
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IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops 2024
In this work, we propose "Temporal 3D ShapE Modeling for VCCRe-ID" (SEMI), a lightweight end-to-end framework that addresses these issues by learning human 3D shape representations.
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification
jurgendn/CVSL_LReID
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IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024
In this paper, we propose "Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification" (CVSL) to address these challenges.
