Person Re-identification: Past, Present and Future

2016/10/10 by Liang Zheng, Yi Yang, Zheng, Liang +3 · 22 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.1610.02984

openalex publication_date 2016/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Person re-identification (re-ID) has become increasingly popular in the community due to its application and research significance. It aims at spotting a person of interest in other cameras. In the early days, hand-crafted algorithms and small-scale evaluation were predominantly reported. Recent years have witnessed the emergence of large-scale datasets and deep learning systems which make use of large data volumes. Considering different tasks, we classify most current re-ID methods into two classes, i.e., image-based and video-based; in both tasks, hand-crafted and deep learning systems will be reviewed. Moreover, two new re-ID tasks which are much closer to real-world applications are described and discussed, i.e., end-to-end re-ID and fast re-ID in very large galleries. This paper: 1) introduces the history of person re-ID and its relationship with image classification and instance retrieval; 2) surveys a broad selection of the hand-crafted systems and the large-scale methods in both image- and video-based re-ID; 3) describes critical future directions in end-to-end re-ID and fast retrieval in large galleries; and 4) finally briefs some important yet under-developed issues.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Open-set Person Re-identification
• ImageNet classification with deep convolutional neural networks
• A Richly Annotated Dataset for Pedestrian Attribute Recognition
• Person Re-identification in the Wild
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal\n Networks
• Learning Multiple Layers of Features from Tiny Images

Cited by

• ReIDMamba: Learning Discriminative Features with Visual State Space Model for Person Re-Identification
• One-Shot Knowledge Transfer for Scalable Person Re-Identification
• Individual identification of brown bears using pose-aware metric learning
• Using predefined vector systems as latent space configuration for neural network supervised training on data with arbitrarily large number of classes
• Progressive and Aligned Pose Attention Transfer for Person Image Generation
• Unity Style Transfer for Person Re-Identification
• Pseudo-positive regularization for deep person re-identification
• Hetero-Center Loss for Cross-Modality Person Re-Identification
• Adversarial Binary Coding for Efficient Person Re-identification
• Person Re-identification with Metric Learning using Privileged Information
• Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking
• Resolution-invariant Person ReID Based on Feature Transformation and Self-weighted Attention
• Domain Agnostic Learning for Unbiased Authentication
• Homocentric Hypersphere Feature Embedding for Person Re-identification
• Video-based Person Re-identification without Bells and Whistles
• Operator-in-the-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification
• Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification
• CORE-ReID: Comprehensive Optimization and Refinement through Ensemble fusion in Domain Adaptation for person re-identification
• Domain Adaptive Attention Learning for Unsupervised Person Re-Identification
• Pedestrian re-identification based on Tree branch network with local and global learning
• EgoReID Dataset: Person Re-identification in Videos Acquired by Mobile Devices with First-Person Point-of-View
• Learning Deep Representations by Mutual Information for Person Re-identification

Related

• Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends
• Deep Learning for Person Re-Identification: A Survey and Outlook
• Deep Learning for Person Re-identification: A Survey and Outlook
• Deep Learning for Video-based Person Re-Identification: A Survey
• Person Re-Identification