Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification in Substation Worker Safety Monitoring

Ensuring safety compliance is paramount in substation operations. However, worker re-identification (Re-ID) remains highly challenging due to severe occlusions, uniform appearance similarity, and substantial illumination variations across shifts and environments. Moreover, the escalating cost of manual identity annotation in large-scale, multi-site surveillance systems necessitates annotation-free approaches for practical deployment. In this paper, we propose AdaInCV (Adaptive Intra-Class Variation Contrastive Learning), an unsupervised Re-ID framework tailored for substation worker safety monitoring. The proposed method quantitatively evaluates the model’s learning capacity for each pseudo-cluster by measuring intra-class feature variation after DBSCAN clustering, and adaptively selects training samples with appropriate difficulty throughout the learning process. To this end, two novel strategies are introduced. Adaptive Sample Mining (AdaSaM) progressively constructs reliable identity clusters while dynamically updating the memory dictionary. Adaptive Outlier Filtering (AdaOF) further exploits informative outlier samples—primarily caused by heavy occlusion or extreme illumination—as hard negatives to enhance contrastive representation learning. Extensive experiments on two widely used Re-ID benchmarks (Market-1501 and MSMT17), as well as an in-house Substation Worker Re-ID (SWRID) dataset, demonstrate that AdaInCV achieves state-of-the-art performance with significantly faster convergence than existing methods, establishing a practical foundation for intelligent safety supervision in power grid operations.