Person Re-ID: Tackling occlusion, labeled data, and privacy

Person re-identification (ReID) has become a critical component in various security and surveillance systems, necessitating accurate and robust identification of individuals across different camera views. The motivation for enhancing ReID systems stems from the need to improve public safety, prevent crime, and support forensic investigations. Despite significant advances, ReID faces several key challenges that hinder its deployment in real-world applications. This review paper addresses key challenges in person re-identification (ReID): overcoming occlusion, reducing dependence on labeled data, and addressing privacy concerns. Various techniques have been explored to tackle these issues effectively. For occlusion, part-based models, LCNNs, attention mechanisms, and adversarial training GANs have demonstrated robustness in capturing person features across diverse and complex environments. To mitigate dependence on labeled data, semi-supervised and unsupervised learning approaches leverage unlabeled data and self-learning capabilities. Additionally, multimodal fusion utilizes complementary information from different sources, significantly enhancing model generalization and performance. Privacy concerns are addressed through federated learning, which fosters collaboration across devices while safeguarding individual data privacy. In summary, this paper highlights diverse technological applications in ReID, offering valuable insights and guidance for future research and practical implementations in the field.