周辺環境の障害物を考慮した深層学習による歩行者の軌道予測
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| Published in: | 日本機械学会論文集 Vol. 87; no. 899; p. 21-00125 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | Japanese |
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一般社団法人 日本機械学会
2021
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| Author | 杉浦, 尚弥 松田, 匠未 黒田, 洋司 |
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| Author_xml | – sequence: 1 fullname: 杉浦, 尚弥 organization: 明治大学大学院 理工学研究科 – sequence: 2 fullname: 松田, 匠未 organization: 明治大学 理工学部 – sequence: 3 fullname: 黒田, 洋司 organization: 明治大学 理工学部 |
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| Copyright | 2021 一般社団法人日本機械学会 |
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| References | Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics, The MIT press (2005). Helbing, D. and Molnar, P., Social force model for pedestrian dynamics, Physical review E, Vol. 51, No. 5 (1995), pp.4282-4286. Mohamed, A., Qian, K., Elhoseiny, M. and Claudel, C., Social-stgcnn: a social spatio-temporal graph convolutional neural network for human trajectory prediction, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2020). Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L. and Savarese, S., Social lstm: human trajectory prediction in crowded spaces, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016), pp.961-971. Kingma, D. P. and Ba, J. L., Adam: a method for stochastic optimization, Proceedings of the International Conference on Learning Representations (ICLR) (2015), pp.1-15. Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H. and Savarese, S., Sophie: an attentive gan for predicting paths compliant to social and physical constraints, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2019). Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P. and Bengio, Y., Graph attention networks, Proceedings of the International Conference on Learning Representations (ICLR)(2017). Rudenko, A.,Kucner, T. P., Swaminathan, C. S., Chadalavada, R. T., Arras, K. O., and Lilienthal, A. J., Thör: human-robot navigation data collection and accurate motion trajectories dataset, IEEE Robotics and Automation Letters, Vol.5, No.2 (2020). Yagi, T., Mangalam, K., Yonetani, R. and Sato, Y., Future person localization in first-person videos, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2018). Hochreiter, S. and Schmidhuber, J., Long short-term memory, Neural computation, Vol.9, No.8 (1997), pp.1735-1780. Huang, Y., Bi, H., Li, Z., Mao, T. and Wang, Z., Stgat: modeling spatialtemporal interactions for human trajectory prediction, Proceedings of the IEEE International Conference on Computer Vision (ICCV)(2019), pp.6272–6281. Takanashi, H., Abe, K., Michitsuji, Y., Shino, M., Raksincharoensak, P. and Hayashi, R., Stochastic prediction model for obstacle avoidance route of pedestrian, Transactions of the JSME (in Japanese), Vol.83,No.855 (2017), DOI:10.1299/transjsme.17-00224. Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S. and Alahi, A., Social gan: socially acceptable trajectories with generative adversarial networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2018). Styles, O., Guha, T. and Sanchez, V., Multiple object forecasting: predicting future object locations in diverse environments, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)(2020), pp.690-699. Clevert, D., Unterthiner, T. and Vandergheynst, P., Fast and accurate deep network learning by exponential linear units(elus), Proceedings of the International Conference on Learning Representations (ICLR)(2016). |
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| SubjectTerms | Attention mechanism Mobile robot Spatial interaction Static obstacle Trajectory prediction |
| Title | 周辺環境の障害物を考慮した深層学習による歩行者の軌道予測 |
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