Person Re-Identification
509 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
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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 with no code
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.
Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification
We introduce the Spatial Cascaded Clustering and Weighted Memory (SCWM) method to address these challenges.
Progressive Contrastive Learning with Multi-Prototype for Unsupervised Visible-Infrared Person Re-identification
To address the problem, we propose a Progressive Contrastive Learning with Multi-Prototype (PCLMP) method for USVI-ReID.
CCPA: Long-term Person Re-Identification via Contrastive Clothing and Pose Augmentation
Long-term Person Re-Identification (LRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing, pose, and viewpoint.
Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification
To facilitate the seamless integration of global classification features with the finely detailed local features selected by DPSM, we introduce a novel feature blending module (FBM).
Contrastive Multiple Instance Learning for Weakly Supervised Person ReID
Through extensive experiments and analysis across three datasets, CMIL not only matches state-of-the-art performance on the large-scale SYSU-30k dataset with fewer assumptions but also consistently outperforms all baselines on the WL-market1501 and Weakly Labeled MUddy racer re-iDentification dataset (WL-MUDD) datasets.
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing
With these datasets and analysis of model limitations, we aim to foster innovations in handling real-world conditions like mud and complex poses to drive progress in robust computer vision.
Attention-based Shape and Gait Representations Learning for Video-based Cloth-Changing Person Re-Identification
Our ASGL framework improves Re-ID performance under clothing variations by learning clothing-invariant gait cues using a Spatial-Temporal Graph Attention Network (ST-GAT).
From Synthetic to Real: Unveiling the Power of Synthetic Data for Video Person Re-ID
In this paper, we study a new problem of cross-domain video based person re-identification (Re-ID).
Spectrum-guided Feature Enhancement Network for Event Person Re-Identification
This network consists of two innovative components: the Multi-grain Spectrum Attention Mechanism (MSAM) and the Consecutive Patch Dropout Module (CPDM).
