Transfer learning for human activity classification in multiple radar setups

Jérémy Fix, Israel Hinostroza, Chengfang Ren, Giovanni Manfredi, Thierry Letertre

Abstract: Deep Learning techniques require vast amount of data for a proper training. In human activity classification using radar signals, the data acquisition can be very expensive and takes a lot of time, but radar databases are starting to be available to the public. In this work we show that we can use these available radar databases to pretrain a neural network that will finish its training on the final radar data even though the radar configuration is different (geometry configuration and carrier frequency).