EvoSTGAT: Evolving spatiotemporal graph attention networks for pedestrian trajectory prediction

Predicting pedestrian trajectory is an essential task in many applications. While previous studies based on graphs seek to model spatiotemporal information among pedestrian interactions, most of them neglect the recursive and continuous relations between neighboring time points. In this paper, we propose an evolving spatiotemporal graph attention network to predict future trajectories of pedestrians. This model considers the evolving relations of social interactions between contiguous time points and uses coordinates. The interaction is modeled by an evolving and dynamic attention mechanism. The social influence of each pedestrians of current frame is evolved from that of last frame and will be utilized to generate the social influence of next frame. The proposed model was tested on two challenging datasets and the experimental results prove the strength of the model.