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Pedestrian Gait Classification Based on Parallel Channel Identity Initialized Recurrent Neural Network

With the increasing demand of security defense, disaster rescue and adjunctive therapy, classification of pedestrian gait has become a hot research topic. Pedestrian gait contains a variety of micro-motions, and the micro-Doppler (m-D) signatures for radar targets can reflect their unique structure information and kinematic characteristics. Therefore, it is of great significance to classify pedestrian gait based on the m-D effect in radar. Aiming at the problem of pedestrian gait classification, this paper proposes a new method based on parallel channel identity initialized recurrent neural network (PC-IRNN). Firstly, the pedestrian gait dataset, including slow walking, normal walking and fast walking, is constructed with m-D spectrograms by the Boulic human walking model and short-time Fourier transform. Secondly, we build the PC-IRNN classifier to classify these gaits. Finally, we compare the long short term memory (LSTM) and identity initialized RNN (IRNN) with the proposed method in terms of classification performance. By testing with leave one user out cross-validation, the experiment results prove the PC-IRNN has lower computation cost and higher accuracy than both LSTM and IRNN.