RLSTM: A Novel Residual and Recurrent Network for Pedestrian Action Classification

Abstract

Properly training LSTMs requires long time and extensive amount of data. To improve the training of these models, this paper proposes a novel residual and recurrent neural network, Resnet-LSTM, for spatio-temporal pedestrian action recognition from image sequences. The model includes a novel layer, called MapGrad, whose goal is improving stationarity of the feature map sequences processed by the ConvLSTM. The paper demonstrates the effectiveness of the proposed model and the MapGrad layer in the spatio-temporal classification of pedestrian actions through an ablation study and comparison with state-of-the-art methods. Overall, RLSTM achieves an accuracy value of 88% and an average precision of 94% on the JAAD dataset, which is a widely used benchmark in the field. Finally, the paper empirically analyzes the effect of increasing input sequence length on standing action recognition, showing that the proposed method yields a recall of 93%.