A Spatiotemporal Detection and Tracing Framework for Human Contact Behavior Using Multicamera Sensors

Social distancing and contact tracing are effective nonpharmaceutical means to ensure public safety and control the rapid spread of infectious diseases. Internet of Things (IoT) sensors can provide reliable data sources for contact tracing, especially in urban public areas. However, existing contact tracing studies mainly use 2-D coordinates or distances to detect direct contact between people and lack indirect contact behavior modeling and sensing in urban 3-D environments. Additionally, an efficient storage and spatiotemporal search method is required to find unsafe contact cases from the large amount of data collected by IoT sensors. This article proposes an innovative spatiotemporal detection and tracing framework for both direct and indirect contact behavior using multicamera sensors. A multicamera coordinate conversion model (M-CCM) is designed to achieve camera calibration and 3-D trajectory aggregation based on a spatiotemporal constraint strategy. Using the aggregated 3-D trajectories, this method further defines several fine-grained characteristics of both direct and indirect contact behavior. A contact graph is designed to model and represent contact activities, which supports efficient spatiotemporal searching and tracing of unsafe contact activities. We have verified the performance of the proposed framework using both a public data set and our data set. Experiments demonstrate that the 3-D trajectory coordinate conversation accuracy was 0.2 m using the proposed M-CCM. The social distance and contact time detection precision and recall are 80% and 93%, respectively, for close contact (<1.5 m). Furthermore, the contact graph can reduce the memory space for monitoring data storage and support efficient spatiotemporal contact tracing.