Case Study: Global Company improves cafeteria queue management with DataArt

olution for Analyzing and Estimating the Queue Size

Global Company partnered with DataArt to address long cafeteria queues in one local office, where a growing workforce was causing employees to waste time, reduce productivity, and experience lower morale during peak lunch hours. DataArt used a computer vision–based approach to help monitor and estimate queue size in real time.

DataArt implemented a continuous tracking system built on OpenCV, TensorFlow/Keras, Python, and time-series analysis. The solution filtered video frames, detected people in the queue, and corrected counts using historical data to improve accuracy, then displayed the line status in the internal corporate system. As a result, the queue became shorter, employees could better plan lunch breaks, and productivity was preserved.