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.
Risaldi et al. (Tue,) studied this question.