Low-cost FMCW radar human-vehicle classification based on transfer learning

Detection and classification of moving targets is an essential feature in many applications like road surveillance systems, autonomous cars, and smart gate systems. Multi-chirp sequence Frequency Modulated Continuous Wave (FMCW) radars with a 2D FFT processing can be used to produce a Range-Doppler images (R-D maps) containing the signature of the target. However, in low-cost FMCW radars, these images suffer from many problems like low-resolution and ambiguity. Such problems can make the image look unrealistic as well as hard to process and classify. In this paper, we propose a human-vehicle classification method based on Transfer Learning. The classification is done by processing the R-D maps generated by a low-cost short range 24 GHz FMCW radar with a convolutional Neural Network (CNN). The adopted CNN succeeded to reach a 96.5% accuracy in discriminating humans from vehicles.