Abstract
Trajectory prediction is a hot topic in the field of computer vision and has a wide range of applications. Trajectory prediction refers to predicting the future trajectory of a target based on its past trajectory. This paper proposes a method based on graph neural network and attention mechanism, in order to update trajectory characteristics by implement global pedestrian interaction. And, a direct relationship between history and future is introduced with the attention module for reducing error propagation. The method was evaluated on several real-world crowd datasets, the results demonstrate the effectiveness of our method.
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Acknowledgement
This work was supported in part by the National Key R&D Program of China (No. 2018YFC0807500), and by Ministry of Science and Technology of Sichuan Province Program (No. 2018GZDZX0048, 20ZDYF0343, 2018HH0075).
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Liu, Z., Zhang, L., Rao, Z., Liu, G. (2020). Attention-Based Interaction Trajectory Prediction. In: Xu, R., De, W., Zhong, W., Tian, L., Bai, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2020. AIMS 2020. Lecture Notes in Computer Science(), vol 12401. Springer, Cham. https://doi.org/10.1007/978-3-030-59605-7_13
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DOI: https://doi.org/10.1007/978-3-030-59605-7_13
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