Real-Time Restaurant Table Occupancy Monitoring System

Abstract

In the fast-evolving landscape of intelligent restaurant management, adopting automation technologies is pivotal to improve operational efficiency and customer experience. Traditional methods of table occupancy detection, which rely on physical sensors, often suffer from high installation costs, limited flexibility, and poor scalability. This research proposes an innovative, vision-based real-time monitoring system for restaurants. The system leverages the YOLOv8objectdetectionmodeltoaccuratelyidentifytablesandhumanpresence,integratedseamlesslywithaDjango-powered multi-tenantwebdashboard.Eachrestaurantcanregisterandconfigureitscustomtablelayout,whichisthenupdatedinreal-time based on live camera feeds. By replacing expensive hardware with a robust AI-driven system, the solution enhances accessibility and scalability. The dashboard offers a user-friendly interface, real-time status updates, and support for simultaneous multi-user access. Extensive testing under varying lighting conditions and scenarios demonstrates the system’s accuracy, reliability, and real-world applicability. The system aims to provide a comprehensive and cost-effective alternative to traditional sensor-based solutions, making advanced occupancy monitoring accessible to a wider range of restaurant businesses.

Introduction

The study proposes a system combining YOLOv8, a high-speed object detection model, with a Django-based web dashboard for real-time table occupancy monitoring tailored for restaurants. Unlike traditional sensor-based or RFID systems that are costly, inflexible, and intrusive, this vision-based solution uses cameras and AI to detect people and tables without physical sensors, offering a scalable, adaptable, and cost-effective alternative.

Each restaurant tenant can independently configure table layouts via a secure Django interface, with data stored in an Oracle SQL database. YOLOv8 processes live video feeds to detect occupancy by calculating the overlap between people and table bounding boxes. Occupancy data is asynchronously sent to the backend and visualized on a real-time dashboard using color codes for quick status assessment.

Performance tests show high detection accuracy (~88-93%) under various conditions, robust adaptability to lighting and occlusion, and acceptable near real-time responsiveness (0.9–1.4 seconds per frame). Temporal filtering is used to stabilize occupancy status, avoiding rapid flickering due to transient detection errors.

Conclusion

The proposed system significantly improves table occupancy monitoring in restaurants using an efficient, cost-effective, and scalable approach. By replacing traditional, expensive, and inflexible sensor-based systems with an AI-driven vision-based solution, it offers a modern and adaptable alternative for restaurant management. YOLOv8’s accuracy in object detection combined with Django’s versatility in web development results in a robust platform suitable for real-world deployment. The multi-tenant architecture, real-time dashboard, and secure data handling capabilities make it a comprehensive solution for enhancing operational efficiency and customer experience in the hospitality industry.

References

[1] Redmon,J.,&Farhadi,A.(2018).YOLOv3:AnIncrementalImprovement.arXiv:1804.02767 Ultralytics YOLOv8 Documentation [2] DjangoProjectDocumentation Oracle SQL Developer Help [3] Liu,B.,etal.(2021).Vision-BasedSeatOccupancyinSmartCafes,IEEEAccess,9,87455–87463

Copyright

Copyright © 2025 Vasudeva Rao Paradesi, Mrs. Ambati Tulasi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.