STM-GCN: a spatiotemporal multi-graph convolutional network for pedestrian trajectory prediction

Pedestrian trajectory prediction has many real-world applications, such as crowd video surveillance and self-driving cars. However, this is a challenging problem due to the complexity of modeling social interactions between road agents (pedestrians and vehicles). Previous studies that addressed trajectory prediction applied traditional deep learning approaches, including CNN and RNN. Meanwhile, the applications of graph neural networks have drawn increasing interest recently, and significant progress has been made in extracting features from complex and unstructured data. In this paper, we propose STM-GCN, a spatiotemporal multi-graph convolutional network for pedestrian trajectory prediction. Our approach consists of collecting information about the social interactions between pedestrians in a crowd that are position-based and velocity-based interactions integrated into a multi-graph. Then we apply Graph Neural Network learning on the obtained multi-graph for predictions. Through experiments on the ETH and UCY benchmarking datasets, our proposed method outperforms the state of the art by 6% average displacement error (ADE) and 10% final displacement error (FDE).