Knowledge Distillation-Driven Lightweight Person Re-Identification Models for Edge Intelligent Surveillance
Keywords:
Knowledge Distillation, Person Re-Identification, Edge Intelligence, Lightweight Deep Learning, Surveillance Systems, Fairness, SustainabilityAbstract
The proliferation of edge intelligent surveillance systems places unprecedented demands on the computational efficiency and deployability of person re-identification models. While deep learning has achieved remarkable accuracy in matching individuals across disjoint camera views, its reliance on large model architectures conflicts with the memory, energy, and latency constraints of distributed edge nodes. Knowledge distillation has emerged as a promising compression paradigm to transfer representational capacity from cumbersome teacher networks into compact student models suitable for edge deployment. This paper presents a system-level examination of distillation-driven lightweight person re-identification, moving beyond standalone accuracy metrics to interrogate the architectural trade-offs, infrastructure design, governance frameworks, fairness implications, and lifecycle sustainability of such models in real-world edge surveillance ecosystems. We analyze how structural choices in teacher–student coupling, multi-expert distillation, and cross-modal alignment influence the equilibrium between inference speed, model fidelity, and robustness to appearance variations including clothing changes. The study also explores the integration patterns within edge-cloud continuums, where hybrid partitioning of recognition pipelines can satisfy both low-latency local inference and global model updating. From a socio-technical perspective, we investigate the fairness risks introduced when compact models are optimized on biased training corpora, the opacity of distillation as a form of model compression for auditing, and the need for governance mechanisms that ensure accountability in automated surveillance. Finally, we consider sustainability indicators such as energy footprint across the distillation and inference lifecycle, advocating for co-design strategies that harmonize environmental costs with operational efficacy. The discussion is anchored in contemporary research on person re-identification, edge intelligence, model compression, and responsible AI, providing a multidisciplinary synthesis that guides future efforts toward ethically sound, operationally viable, and structurally resilient lightweight surveillance analytics.
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