Relation Reasoning for Video Pedestrian Trajectory Prediction

Haowen Tang, Ping Wei, Huan Li, Jiapeng Li, Nanning Zheng

Abstract: Pedestrian trajectory prediction is a challenging and important task in many applications, which aims to predict future pedestrians' trajectory coordinates from the input historical data. The existing methods usually use ready-made trajectory coordinates as inputs, which is, however, unavailable in video-based scenarios. In this paper, we propose a relation reasoning hypergraph (RRH) model to directly predict multiple pedestrian trajectories from raw videos. It is a challenging issue for the input and output are in different modalities and a video may contain multiple pedestrians. Our model integrates historical trajectory tracking, pedestrian relation reasoning, and future trajectory prediction into one framework. For capturing the subtle social relationships among pedestrians, we design a relation reasoning hypergraph network. We tested the proposed method on two public pedestrians datasets and the performance demonstrates the power of the model.