Causality and “In-the-Wild” Video-Based Person Re-Identification: A Survey
Md Rashidunnabi, Kailash Hambarde, Hugo Proença · Electronics · 2025
Video-based person re-identification (re-identification) remains underused in real-world deployments, despite impressive benchmark performance. Most existing models rely on superficial correlations—such as clothing, background, or lighting—that fail to generalize across domains, viewpoints, and temporal variations. This study examines the emerging role of causal reasoning as a principled alternative to traditional correlation-based approaches in video-based re-identification. We provide a structured and critical analysis of methods that leverage structural causal models (SCMs), interventions, and counterfactual reasoning to isolate identity-specific features from confounding factors. This study is organized around a novel taxonomy of causal re-identification methods, spanning generative disentanglement, domain-invariant modeling, and causal transformers. We review current evaluation metrics and introduce causal-specific robustness measures. In addition, we assess the practical challenges—scalability, fairness, interpretability, and privacy—that must be addressed for real-world adoption. Finally, we identify open problems and outline future research directions that integrate causal modeling with efficient architectures and self-supervised learning. This study aims to establish a coherent foundation for causal video-based person re-identification and catalyze the next phase of research in this rapidly evolving domain.