STENet: A hybrid spatio-temporal embedding network for human trajectory forecasting

Abstract In this paper, we present a hybrid spatio-temporal embedding network (named as STENet) for human trajectory forecasting, which is built upon a GAN-based hierarchical framework. Differently from traditional approaches that only use LSTM for trajectory modeling, we exploit the 1D Convolutional Neural Network (1D-CNN) to embed position features at multiple temporal scales. Moreover, we propose a two-stage graph attention mechanism, which can better describe mutual interactions among pedestrians in the crowd. Additionally, group influences at every time step are taken into account as well. The overall framework is designed using a hierarchical manner, and trained using the Wasserstein distance. We carry out our experiments on the ETH and the UCY datasets. The corresponding results demonstrate the effectiveness of the proposed framework.