Abstract
This paper reports a machine learning approach for people detection and tracking in indoor environments using a compact radar system deployed by a mobile robot. The set-up described in the paper includes a series of experiments carried out in an indoor scenario involving walking people and dummies representative of other moving objects. In these experiments, distinct learning models (a neural network and a random forest) were explored with different combinations of radar features to achieve person versus non-person classification.
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Acknowledgement
This work is funded by Research Project RETIOT PT2020-03/SAICT/2015 - Fundação para a Ciência e Tecnologia.
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Castanheira, J., Curado, F., Pedrosa, E., Gonçalves, E., Tomé, A. (2020). Machine Learning Methods for Radar-Based People Detection and Tracking by Mobile Robots. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-36150-1_31
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DOI: https://doi.org/10.1007/978-3-030-36150-1_31
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