Introvert: Human Trajectory Prediction via Conditional 3D Attention

Predicting human trajectories is an important component of autonomous moving platforms, such as social robots and self-driving cars. Human trajectories are affected by both the physical features of the environment and social interactions with other humans. Despite recent surge of studies on human path prediction, most works focus on static scene information, therefore, cannot leverage the rich dynamic visual information of the scene. In this work, we propose Introvert, a model which predicts human path based on his/her observed trajectory and the dynamic scene context, captured via a conditional 3D visual attention mechanism working on the input video. Introvert infers both environment constraints and social interactions through observing the dynamic scene instead of communicating with other humans, hence, its computational cost is independent of how crowded the surrounding of a target human is. In addition, to focus on relevant interactions and constraints for each human, Introvert conditions its 3D attention model on the observed trajectory of the target human to extract and focus on relevant spatio-temporal primitives. Our experiments on five publicly available datasets show that the Introvert improves the prediction errors of the state of the art.