Building occupancy estimation using single channel CW radar and deep learning

Counting the number of people in a room is crucial for optimizing smart buildings, enhancing energy efficiency, and ensuring security while preserving privacy. This study introduces a novel radar-based occupancy estimation method leveraging a 24-GHz Continuous Wave (CW) radar system integrated with time-frequency mapping techniques using Continuous Wavelet Transform (CWT) and power spectrum analysis. Unlike previous studies that rely on WiFi or PIR-based sensors, this approach provides a robust alternative without privacy concerns. The time-frequency scalograms generated from radar echoes were used to train deep-learning models, including DarkNet19, MobileNetV2, and ResNet18. Experiments conducted with sedentary occupants over 4 hours and 40 minutes resulted in 1680 image samples. The proposed approach achieved high accuracy, with DarkNet19 performing the best, reaching 92.7% on CWT images and 92.3% on power spectrum images. Additionally, experiments in a walking environment with another continuous 1 hour of data achieved 86.5% accuracy, demonstrating the method's effectiveness beyond static scenarios. These results confirm that CW radar with deep learning can enable non-intrusive, privacy-preserving occupancy estimation for smart building applications.