Real-Time Table Availability Detection in Dynamic Dining Environments Using YOLOv8 and Geometric Overlap Analysis

Muhammad Risaldi; Ayu Safitri; Andi Baso Kaswar; Muhammad Fajar B; Dyah Darma Andayani; Fhatiah Adiba; Firdaus; Jumadi M Parenreng

doi:10.46604/peti.2026.15704

Authors

Muhammad Risaldi Department of Computer Engineering, State University of Makassar, Makassar, Indonesia https://orcid.org/0009-0001-8270-7071
Ayu Safitri Department of Computer Engineering, State University of Makassar, Makassar, Indonesia https://orcid.org/0009-0008-6387-8693
Andi Baso Kaswar Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Muhammad Fajar B Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Dyah Darma Andayani Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Fhatiah Adiba Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Firdaus Department of Electrical Engineering, State University of Makassar, Makassar, Indonesia
Jumadi M Parenreng Department of Computer Engineering, State University of Makassar, Makassar, Indonesia

DOI:

https://doi.org/10.46604/peti.2026.15704

Keywords:

geometric overlap, object detection, real-time monitoring, table availability, YOLOv8

Abstract

This study addresses the challenge of determining table availability in dynamic restaurant environments. Customer mobility, visual obstruction, and irregular table configurations make direct visual classification ineffective. A real-time table availability detection system utilizing YOLOv8 and simple online and real-time tracking (SORT) is proposed to address these difficulties. The primary innovation is a geometric overlap-based inference technique for table status assessment. The method examines the spatial link between customer centroids and table polygons. Centroid area expansion is applied to mitigate bounding box noise. The system is evaluated using an annotated dataset and compared with a direct detection baseline. Experimental findings indicate that the method attains an accuracy of 91.08%, markedly surpassing the baseline accuracy of 35.75%. Real-time performance assessment indicates a processing speed of 14.58 FPS with a latency of 68.57 ms during CPU-only execution. This performance satisfies real-time criteria. The study demonstrates that the method offers a dependable and efficient alternative for automated table availability monitoring.

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