Design of an IoT-Based Cross-Modality Pedestrian Monitoring System for Contact Tracing in COVID-19 Prevention

COVID-19 epidemic prevention and control has become a regular part of life, and tracking people’s trajectory (especially fever patients) in public areas can help curb the spread of the epidemic. IoT technology has dramatically improved the efficiency of epidemic prevention and control. In this paper, we propose an IoT-based cross-modality pedestrian surveillance system architecture with the following characteristics: (1) robust, the system accesses multiple modal information (visible light, infrared, temperature, mobile phone signals), and can achieve trajectory tracking in non-cooperative situations; (2) with flexibility, we develop a nodal visual artificial intelligence software platform for deep neural network training and optimization, using which the deployment can be iteratively optimized intuitively and quickly. Finally, we also discuss the research prospects of artificial intelligence and IoT technologies in the direction of epidemic prevention and control.