Vision-based motion prediction for construction workers safety in real-time multi-camera system

Ensuring worker safety on dynamic construction sites is a significant challenge, especially as it is crucial to immediately identify potential hazards and warn workers. Existing computer vision-based motion prediction methods often overlook the false negative issue caused by the noisy environments of construction sites, and treat tracking and trajectory prediction as disconnected processes. This study introduces MPSORT, a method that suggests trajectory prediction-based tracking with trajectory interpolation for vision-based automated safety monitoring. The proposed method predicts the future movements of construction workers and vehicles using multiple CCTV cameras, and localizes these predictions onto the construction site's bird's eye view (BEV) map. This enables to send the real-time warnings to workers in danger, preventing accidents such as collision, fall, and getting stuck. We evaluated the performance of our method in both object tracking and trajectory prediction tasks on dataset from multiple CCTV cameras on construction sites. The object tracking results show an approximate 10% increase in the number of tracked objects, and the trajectory prediction results indicate an ADE of 7.138 and an FDE of 12.493, reflecting improvements of more than 5% and 2% in ADE and FDE, respectively, compared to previous methods. Overall, these findings are significant for minimizing accidents and enhancing safety and efficiency on construction sites.