Intelligent emergency digital twin system for monitoring building fire evacuation

Abstract

The provision of real-time and detailed evacuation information feedback is vitally significant for the formulation and adaptation of the onsite evacuation strategy. The conventional surveillance system relying on surveillance cameras is limited to processing video and fails to extract human behaviour or provide privacy protection. This work proposes an Intelligent Emergency Digital Twin system based on computer vision and deep learning. The system comprises (1) CCTV network, (2) YOLOv4 evacuee detector, (3) DeepSORT evacuee tracker, (4) Perspective transformer, and (5) Digital Twin interface. It enables the detection and tracking of evacuees, the calculation of their egress speed, and the protection of their privacy in a digital interface. The proposed system was evaluated in a staircase of an office building through two types of tests with positive results: ratio of successfully detecting is 100% in individual objects test and about 90% in multiple objects test. The evacuation data generated by the digital twin system would be useful for guiding evacuations out of fire scenarios. This proposed digital twin framework can lay the foundation for the implementation of smart human monitoring in fire scenarios for buildings.