Semantic Prior-Guided Period-Aware Multi-Expert Segmentation for Long-Term Fixed-View Visual Monitoring

Accurate semantic segmentation is essential for long-term fixed-view monitoring, where seasonal, illumination, weather, and environmental changes alter local appearance while the global scene layout remains relatively stable. To address this structure-appearance modeling problem, we propose SPMES, a Semantic Prior-Guided Period-Aware Multi-Expert Segmentation framework. SPMES comprises a Global Expert that learns stable semantic-prior maps from the complete training set, a Semantic-Guided Fusion Module that injects these priors into the input representation, and period-specific experts that model recurring appearance characteristics according to acquisition time. Experiments on a long-term fixed-view monitoring dataset collected for this study evaluate SPMES with U-Net, U-Net++, U²-Net^†, Swin-Unet, and Mamba-UNet. Compared with the corresponding baselines, SPMES obtains better point estimates across all six evaluation metrics for each backbone, although the magnitude of the changes varies across architectures and metrics. Ablation studies show that individual configurations exhibit metric- and backbone-dependent effects, whereas the complete integration of global semantic priors, semantic-guided fusion, and period-specific learning provides the best overall balance. These results support the effectiveness and backbone-level compatibility of SPMES within the studied monitoring setting.