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.
CITATION STYLE
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 Advances in Intelligent Systems and Computing (Vol. 1093 AISC, pp. 379–391). Springer. https://doi.org/10.1007/978-3-030-36150-1_31
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