Vision-Language Guided Identity Retrieval with Robust Appearance Disentanglement for Long-Term Person Tracking
Keywords:
person re-identification, vision-language models, appearance disentanglement, long-term tracking, system architecture, fairness, sustainable AIAbstract
The sustained tracking of individuals across distributed camera networks over extended temporal windows remains an enduring systems challenge, as visual appearance can vary markedly due to clothing changes, illumination shifts, and occlusions. This paper offers a comprehensive system-level investigation into the convergence of vision-language guidance and robust appearance disentanglement for long-term person re-identification. We posit that integrating pre-trained vision-language models into the retrieval pipeline injects semantic reasoning about personal attributes that are resilient to superficial appearance transformations. A hybrid architecture is proposed in which multimodal fusion modules align visual embeddings with natural language descriptions of identity-relevant traits, while adversarial feature separation and semantic consistency regularization enforce identity-salient representations decoupled from transient clothing styles. The discussion moves beyond algorithmic design to encompass the governance, ethical deployment, and sustainability of such tracking infrastructures. We analyze structural trade-offs between model expressiveness and inference latency, energy-aware compression strategies, and privacy-preserving data flows. Drawing on smart city and public safety case illustrations, the paper examines how fairness audits and policy-aligned architectures can mitigate risks of demographic bias and erosion of civil liberties. The resulting framework provides a roadmap for designing person tracking systems that balance accuracy, robustness, accountability, and environmental responsibility in long-term operational contexts.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
