Human Activities Classification Based on Complex-Value Convolutional Neural Network

摘要

The classification and recognition of human activities plays an important role in the fields of security, crime prevention, medical surveillance, etc. The swings of the limbs during the movement of the human body introduce rich micro-Doppler information. Different human activities correspond to different micro-Doppler distributions. Therefore, the discrepancies in micro-Doppler distributions can be utilized to classify different human activities. At the same time, the deep learning method based on image domain has been widely utilized in the classification and recognition of radar targets. To improve human activities classification performance, this paper proposes a human activities classification method based on complex-value convolutional neural network (CV-CNN). The motion capture data are utilized to obtain the motion trajectories of various parts of the human, and then the radar echoes of human can be got. After that, the time-frequency (TF) analysis is utilized to obtain the TF representations, and the micro-Doppler characteristics are analyzed to establish a human activities sample database. A CV-CNN structure is built to train the complex-value TF data of different human activities. The experiment results which the classification accuracy reaches 99.81 verify the effectiveness of the proposed method. The results under different training sample proportions and SNRs demonstrate the robustness of the proposed method, which is 14.22 higher than that of CNN when the ratio of training set is 10, and 11.34 higher than that of CNN when the SNR is -5dB.