<![CDATA[Overtourism has emerged as a critical issue in popular tourist destinations, often leading to environmental strain, reduced visitor satisfaction, and safety concerns. Traditional methods such as ticket counts, or vehicle estimation fail to provide real-time insights or adapt effectively to dynamic outdoor environments. This study proposes a privacy-aware, real-time visitor capacity monitoring system for smart tourism, utilizing YOLOv8-based head detection and Centroid Tracking to ensure accurate, non-intrusive people counting in dense and complex crowd scenarios. Head detection is employed specifically to preserve personal privacy without compromising on detection performance. The system was trained on a custom dataset comprising over 3,000 annotated frames with diverse lighting conditions, occlusion levels, and viewing angles. Deployment at Wana Wisata Kawah Putih, an open-air tourist destination in Indonesia, demonstrated strong performance with 94.2% accuracy, 95.1% precision, and 90.6% recall, while sustaining >60 FPS for real-time execution. The integration of Centroid Tracking enables lightweight, frame-to-frame identity association with minimal computational overhead, making the system suitable for deployment on moderate-performance hardware. Despite its robustness, the system's performance slightly degrades under extreme weather (e.g., fog, direct glare) and rapid lighting transitions, which remain challenges for visual models. Moreover, the current model requires further evaluation for cross-location generalizability. Future research will explore the integration of predictive analytics for visitor flow forecasting, and further optimization of energy efficiency and adaptive detection under environmental uncertainty. This work contributes a scalable, ethical solution for real-time crowd monitoring to support informed, sustainable tourism management.]]>
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