Vehicle classification based on convolutional networks applied to FM-CW radar signals
Computer Vision and Pattern Recognition
2018-04-23 v3
Abstract
This paper investigates the processing of Frequency Modulated-Continuos Wave (FM-CW) radar signals for vehicle classification. In the last years deep learning has gained interest in several scientific fields and signal processing is not one exception. In this work we address the recognition of the vehicle category using a Convolutional Neural Network (CNN) applied to range Doppler signature. The developed system first transforms the 1-dimensional signal into a 3-dimensional signal that is subsequently used as input to the CNN. When using the trained model to predict the vehicle category we obtain good performance.
Cite
@article{arxiv.1710.05718,
title = {Vehicle classification based on convolutional networks applied to FM-CW radar signals},
author = {Samuele Capobianco and Luca Facheris and Fabrizio Cuccoli and Simone Marinai},
journal= {arXiv preprint arXiv:1710.05718},
year = {2018}
}
Comments
in Proceedings of 1st European Conference on Traffic Mining Applied to Police Activities (TRAP 2017)