Smart building evacuation by tracking multi-camera network and explainable Re-identification model
Yifei Ding, Xinghao Chen, Yuxin Zhang, Xinyan Huang
Abstract
Real-time crowd data from surveillance devices is essential for the emergency decision-making and management inside complex buildings. Traditional evacuation monitoring with single-camera tracking often leads to erratic information, so multi-camera tracking for building occupants is critical to enhance evacuation safety and emergency response. This research proposes a novel real-time multi-camera tracking framework for the detection, tracking and re-identification (Re-ID) of evacuees across multi-camera. The framework consists of (1) a multi-camera network, (2) human detection model, (3) tracking model, (4) an explainable attention-aided Re-ID (AAR) model, and (5) a module of feature matching and re-distribution algorithm. The attention-aided Re-ID model presents outstanding performance on both the standard big benchmarks and our custom dataset. Moreover, a simple evacuation drill is conducted to demonstrate real-time multi-camera tracking, showing good accuracy in Re-ID and personnel counting, where the overall Re-ID tracking accuracy exceeds 75% and the personnel counting accuracy is approaching 100%. Lastly, the class activation map (CAM) illustrates the model explainability and limitations. The proposed multi-camera tracking framework helps develop a more automated monitoring system and an intelligent digital twin for building emergency safety management. • Establish a multi-camera re-identification (Re-ID) framework for monitoring building evacuation. • Propose an explainable attention-aided Re-ID network to enhance human feature extraction ability. • Develop a novel Re-ID dataset annotation tool for building emergency management and digital twin. • Achieve multi-camera monitoring walking evacuees with constant identities and personnel counting.