(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
Person re-identification in distributed camera networks forms a cornerstone of modern smart surveillance, enabling persistent tracking of individuals across disjoint views for security, forensics, and operational analytics. Yet a fundamental vulnerability undermines current systems: reliance on static appearance cues that break down when subjects change clothing. This paper addresses the clothing-invariant re-identification challenge through a cross-modal contrastive learning framework conceived not merely as an algorithmic novelty but as an entire system-level reconfiguration of sensing, representation, and deployment. We examine how multi-modal streams—visible spectrum, infrared, depth, and gait dynamics—can be aligned in a shared embedding space using contrastive objectives that enforce invariance to clothing while preserving identity-discriminative structure. The discussion is oriented around structural trade-offs in the architecture, the decoupling of feature-driven and multimodal fusion attention, and the embedding of such models within heterogeneous edge-cloud infrastructures. We analyze deployment concerns including real-time inference under resource constraints, privacy-preserving federated learning, resilience to environmental and adversarial perturbations, and fairness across demographic subgroups that may be disproportionately affected by appearance changes. Governance, algorithmic transparency, and energy sustainability are interconnected dimensions that we argue must be integrated into the design cycle of next-generation surveillance AI. By reframing clothing-invariant person re-identification as a socio-technical system problem, this paper provides a comprehensive blueprint for engineers, policymakers, and infrastructure architects striving for robust, equitable, and maintainable smart surveillance ecosystems.