  Predicting trajectories of pedestrians is quintessential for autonomous
robots which share the same environment with humans. In order to effectively
and safely interact with humans, trajectory prediction needs to be both precise
and computationally efficient. In this work, we propose a convolutional neural
network (CNN) based human trajectory prediction approach. Unlike more recent
LSTM-based moles which attend sequentially to each frame, our model supports
increased parallelism and effective temporal representation. The proposed
compact CNN model is faster than the current approaches yet still yields
competitive results.
