Multi target human gait classification using deep convolutional neural networks on micro doppler spectrograms
This paper presents the use of a deep convolutional neural network (DCNN) in distinguishing between absence of human gait and the presence of single or multiple instances of human gait by applying the DCNN to micro Doppler spectrograms. The approach is evaluated for various radar frequencies and SNR levels using model data, while final validation is performed using X band CW radar measurements. Satisfactorily results are obtained by the DCNN while outperforming a few conventional classifiers, showing the possible benefits of deep learning approaches in human gait classification.
2016 European Radar Conference (EuRAD)