Deep learning-driven digital twin system for pedestrian tracking and evacuation load assessment in public spaces

Real-time pedestrian localization is essential for effective emergency evacuation in large indoor public spaces. This study presents an intelligent digital twin system for evacuation monitoring, integrating deep learning and computer vision. The system includes four components: (1) Internet of Things sensor network, (2) cloud computing server, (3) Artificial Intelligence processing engine, and (4) interactive user interface. The Artificial Intelligence engine introduces three innovations: automated detection and tracking of pedestrian coordinates using You Only Look Once-Pose (YOLO-Pose) and Deep Simple Online and Realtime Tracking (DeepSORT); transformation of multi-camera data into a unified world coordinate system; and the Multi-Object Matching Operation (MOMO) algorithm for identity association. These enable accurate detection, localization, and counting while minimizing identifiability. The system was validated in controlled experiments and a high-speed rail station waiting hall with dense, dynamic pedestrian flow. It achieves high localization precision, with a root mean square error of 5.3 cm, a mean absolute error of 4.8 cm, and a people counting accuracy of 92.34% while processing 30 frames per second video at 27.8 ms per frame. These results demonstrate the potential of the digital twin framework in intelligent evacuation management. The main contribution in Artificial Intelligence is the Multi-Object Matching Operation algorithm, and the engineering contribution is the realization of a real-time digital twin system in a large public facility. • Proposes a digital twin system integrating YOLO-Pose and DeepSORT for real-time pedestrian tracking. • Introduces a novel multi-camera calibration method for global coordinate unification. • Achieves 92.34% people counting accuracy in complex public infrastructure environments.