Long-Term Performance Degradation and Maintenance in Urban Visual Surveillance Systems

Abstract

Urban surveillance systems rely heavily on visual analysis pipelines for traffic monitoring, security inspection, and incident detection. Over long deployment periods, changes in lighting infrastructure, seasonal conditions, and camera aging gradually affect image quality and model reliability. This study analyzes video data collected from 420 fixed cameras operating continuously over three years. Variations in illumination intensity, color temperature, and shadow distribution were quantified and correlated with detection accuracy. Results indicate that pedestrian and vehicle detection precision declined by up to 17% in areas where nighttime lighting was upgraded without recalibration. A periodic visual normalization and model fine-tuning scheme was introduced to mitigate degradation. After deployment, performance fluctuations were reduced, but maintenance costs increased due to additional calibration requirements. Sustained visual analytics accuracy depends on coordinated hardware and software management.