Abstract
Video-based person re-identification (Re-ID) remains brittle 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 survey examines the emerging role of causal reasoning as a principled alternative to traditional correlation-based approaches in video-based Re-ID. We provide a structured and critical analysis of methods that leverage structural causal models, interventions, and counterfactual reasoning to isolate identity-specific features from confounding factors. The survey is organized around a novel taxonomy of causal Re-ID methods that spans generative disentanglement, domain-invariant modeling, and causal transformers. We review current evaluation metrics and introduce causal-specific robustness measures. In addition, we assess practical challenges of 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 survey aims to establish a coherent foundation for causal video-based person Re-ID and to catalyze the next phase of research in this rapidly evolving domain.
Abstract (translated)
基于视频的人再识别（Re-ID）在实际部署中尽管基准性能表现出色，但依然脆弱。大多数现有模型依赖于肤浅的相关性，如服装、背景或光照等特征，这些特征无法跨领域、视角和时间变化进行泛化。本文综述探讨了因果推理作为传统相关性方法原则替代方案的新兴角色，在基于视频的人再识别中发挥作用。我们提供了对利用结构因果模型、干预措施和反事实推理的方法进行结构化且批判性的分析，以从混淆因素中隔离身份特定特征。本综述围绕新颖的因果Re-ID方法分类组织展开，该分类涵盖了生成性解缠、域不变建模以及因果变压器等领域。此外，我们回顾了当前评估指标，并引入了针对因果特性的鲁棒性度量。同时，我们也评估了实现规模扩展、公平性、可解释性和隐私等实际挑战，这些对于现实世界的采用至关重要。最后，本文确定了开放问题并概述了未来研究方向，旨在将因果建模与高效架构及自监督学习相结合。 该综述旨在为基于视频的人再识别的因果方法建立一个连贯的基础，并推动这一迅速发展的领域的下一阶段研究。
URL
https://arxiv.org/abs/2505.20540
