Context-adaptable radar-based people counting via few-shot learning Mauro, Gianfranco Pegalajar Cuéllar, Manuel Morales Santos, Diego Pedro Active learning Meta learning Radar Few shot learning People counting Weighting network This work has received funding from the ECSEL Joint Under-taking (JU) under grant agreement No. 876925 (ANDANTE). The JU receives support from the European Union's Horizon 2020 research and innovation programme and France, Belgium, Germany, Netherlands, Portugal, Spain, Switzerland. Funding for open access publishing: Universidad de Granada/CBUA. 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. 2023-09-25T12:06:00Z 2023-09-25T12:06:00Z 2023-08-08 journal article Mauro, G., Martinez-Rodriguez, I., Ott, J. et al. Context-adaptable radar-based people counting via few-shot learning. Appl Intell (2023). [https://doi.org/10.1007/s10489-023-04778-z] https://hdl.handle.net/10481/84647 10.1007/s10489-023-04778-z eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer Nature