Training a CNN on simulated point clouds augmented by removing segments improves real-world casualty detection accuracy from 83% to 91%.
Multisensor low-cost system for real time human detection and remote respiration monitoring,
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Sim-to-Real Learning for Casualty Detection from Ground Projected Point Cloud Data
Training a CNN on simulated point clouds augmented by removing segments improves real-world casualty detection accuracy from 83% to 91%.