Human Trajectory Prediction Based on a Single Frame of Pose and Initial Velocity Information

Predicting human motion is a fundamental part of many applications such as animation and human–robot interaction. We propose a novel recurrent neural network (RNN) model, which can predict the trajectory of human movement using a single frame of the pose information and the initial velocity. This contrasts with previous works that required multiple frames of a person’s past motion history to predict future sequences. Our method leverages the trajectory and pose information to predict the most likely motion sequences, overcoming the challenge of ambiguity due to the high variation in poses. In this work, we demonstrate that our method is capable of learning the human’s movement and predicting the human’s trajectory. The human body is divided into five parts to study the relationships among the internal motions. This enables a better model to predict future trajectories according to the current human pose. Our approach surpasses several baseline methods on the Human 3.6M dataset and achieves state-of-the-art performance.