Abstract
This paper describes the work developed towards the implementation of a radar-based system for people detection and tracking in indoor environments using machine learning techniques. For such, a series of experiments were carried out in an indoor scenario involving walking people and dummies representative of other moving objects. The applied machine learning methods included a neural network and a random forest classifier. The success rates (accuracies) obtained with both methods using the experimental data sets evidence the high potential of the proposed approach.
Funded by Research Project RETIOT PT2020-03/SAICT/2015 - Fundação para a Ciência e Tecnologia.
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Castanheira, J., Curado, F., Tomé, A., Gonçalves, E. (2019). Machine Learning Methods for Radar-Based People Detection and Tracking. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_35
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DOI: https://doi.org/10.1007/978-3-030-30241-2_35
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