Customer Activity Detection Using YOLOv8 and Status Order Algorithm

This paper presents a method to analyze table utilization and customer movements in fast food courts through video surveillance data using methods from machine learning. The system that has been proposed YOLOv8 for object detection, DeepSORT for object tracking and a custom status order algorithm to classify the table activities. A real-world implementation on existing infrastructure in SA such as the Tapah Southbound RSA (Rest and Service Area) Food Court were undertaken to address challenges posed by non-optimal camera angles, occlusion issues etc. Based on people (people) and objects (bowls, plates, bottles or cups), the system classifies table statuses to eat, drink, eat_drink sit-down empty. To improve the classification accuracy and to address this unstable detection issue, a status order hierarchy was introduce. This paper summarizes the system performance of occupancy rates, activity durations and customer patterns analysis while highlighting its limitations by potential embedding in started a business intelligence for food court operations. Consistent with our confirmation, we find high table utilization rates and clear behavioural patterns that can provide insights for service optimization or layout improvement. It also points to avenues for further improvement in data collection methods that cater to improved detection of nuanced activity under challenging visual settings.