@article{ref1,
title="MDST-DGCN: a multilevel dynamic spatiotemporal directed graph convolutional network for pedestrian trajectory prediction",
journal="Computational intelligence and neuroscience",
year="2022",
author="Liu, Shaohua and Liu, Haibo and Wang, Yisu and Sun, Jingkai and Mao, Tianlu",
volume="2022",
number="",
pages="e4192367-e4192367",
abstract="Pedestrian trajectory prediction is an essential but challenging task. Social interactions between pedestrians have an immense impact on trajectories. A better way to model social interactions generally achieves a more accurate trajectory prediction. To comprehensively model the interactions between pedestrians, we propose a multilevel dynamic spatiotemporal digraph convolutional network (MDST-DGCN). It consists of three parts: a motion encoder to capture the pedestrians' specific motion features, a multilevel dynamic spatiotemporal directed graph encoder (MDST-DGEN) to capture the social interaction features of multiple levels and adaptively fuse them, and a motion decoder to produce the future trajectories. Experimental results on public datasets demonstrate that our model achieves state-of-the-art results in both long-term and short-term predictions for both high-density and low-density crowds.<p /> <p>Language: en</p>",
language="en",
issn="1687-5265",
doi="10.1155/2022/4192367",
url="http://dx.doi.org/10.1155/2022/4192367"
}