Abstract
Human trajectory prediction is a popular research of computer vision and widely used in robot navigation systems and automatic driving systems. The existing work is more about modeling the interactions among pedestrians from the perspective of spatial relations. The social relation between pedestrians is another important factor that affects interactions but has been neglected. Motivated by this idea, we propose a Social Relational Graph Attention Network (SRGAT) via seq2seq architecture for human trajectory prediction. Specifically, relational graph attention network is utilized to model social interactions among pedestrians with different social relations and we use a LSTM model to capture the temporal feature among these interactions. Experimental results on two public datasets (ETH and UCY) prove that SRGAT achieves superior performance compared with recent methods and the predicted trajectories are more socially plausible.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61972128), the Fundamental Research Funds for the Central Universities of China (Grant No. PA2019GDPK0071).
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Peng, Y., Zhang, G., Li, X., Zheng, L. (2021). SRGAT: Social Relational Graph Attention Network for Human Trajectory Prediction. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_54
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DOI: https://doi.org/10.1007/978-3-030-92270-2_54
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