Egocentric Pedestrian Trajectory Prediction

Abstract

Pedestrian trajectory prediction is one of the important research topics for autonomous vehicles. Many factors such as Pedestrians' actions and waypoints in the scene can affect the pedestrians' routes. Currently, most researches focus on predicting trajectories in Bird's Eye View (BEV), which limits the application in monocular cameras. In this paper, we propose to combine BEV trajectory prediction with deterministic tracking method and create a new egocentric algorithm for predicting pedestrian trajectories. Experiment results showed that our method can accurately predict pedestrian trajectories in egocentric scenes.