@misc{10481/84647,
year = {2023},
month = {8},
url = {https://hdl.handle.net/10481/84647},
abstract = {In many industrial or healthcare contexts, keeping track of the number of people is essential. Radar systems, with their low overall cost and power consumption, enable privacy-friendly monitoring in many use cases. Yet, radar data are hard to interpret and incompatible with most computer vision strategies. Many current deep learning-based systems achieve high monitoring performance but are strongly context-dependent. In this work, we show how context generalization approaches can let the monitoring system fit unseen radar scenarios without adaptation steps. We collect data via a 60 GHz frequency-modulated continuous wave in three office rooms with up to three people and preprocess them in the frequency domain. Then, using meta learning, specifically the Weighting-Injection Net, we generate relationship scores between the few training datasets and query data. We further present an optimization-based approach coupled with weighting networks that can increase the training stability when only very few training examples are available. Finally, we use pool-based sampling active learning to fine-tune the model in new scenarios, labeling only the most uncertain data. Without adaptation needs, we achieve over 80% and 70% accuracy by testing the meta learning algorithms in new radar positions and a new office, respectively.},
organization = {ECSEL Joint Under-taking (JU)
	876925},
organization = {Horizon 2020},
organization = {Universidad de Granada/CBUA},
publisher = {Springer Nature},
keywords = {Active learning},
keywords = {Meta learning},
keywords = {Radar},
keywords = {Few shot learning},
keywords = {People counting},
keywords = {Weighting network},
title = {Context-adaptable radar-based people counting via few-shot learning},
doi = {10.1007/s10489-023-04778-z},
author = {Mauro, Gianfranco and Pegalajar Cuéllar, Manuel and Morales Santos, Diego Pedro},
}