The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Social Attention: Modeling Attention in Human Crowds
Abstract
Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd.
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Cites
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
Social force model for pedestrian dynamics
Helbing, D., & Molnár, P. (1995) · Physical Review E · doi:10.1103/physreve.51.4282 · Cited by 6,845
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.110 · Cited by 3,483
Generating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894
You'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 5×
“1) Average Displacement Error: Similar to the metric used in [23], this computes the mean euclidean distance over all estimated points at each time-step in the”
BackgroundMethodInfluentialAbnormal crowd behavior detection using social force model
Mehran, R., Oyama, A., & Shah, M. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206641 · Cited by 1,672
Lerner, A., Chrysanthou, Y., & Lischinski, D. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01089.x · Cited by 1,171
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Jain, A., Zamir, A. R., Savarese, S., & Saxena, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.573 · Cited by 1,133
A System for the Notation of Proxemic Behavior1
HALL, E. T. (1963) · American Anthropologist · doi:10.1525/aa.1963.65.5.02a00020 · Cited by 1,006
Recurrent Network Models for Human Dynamics
Fragkiadaki, K., Levine, S., Felsen, P., & Malik, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.494 · Cited by 860
Cited by
Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data
Salzmann, T., Ivanovic, B., Chakravarty, P., & Pavone, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58523-5_40 · Cited by 1,076
STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction
Huang, Y., Bi, H., Li, Z., Mao, T., & Wang, Z. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00637 · Cited by 658
Human motion trajectory prediction: a survey
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917446 · Cited by 658
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Yuan, Y., Weng, X., Ou, Y., & Kitani, K. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00967 · Cited by 631
Chen, C., Liu, Y., Kreiss, S., & Alahi, A. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8794134 · Cited by 615
Metadata
- Authors
- Vemula, A., Muelling, K., & Oh, J.
- Venue
- 2018 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 4601-4607
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2766836212
- Cited by
- 675
- References
- 30
- FWCI
- 146.81
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Vemula, A., Muelling, K., & Oh, J. (2018). Social Attention: Modeling Attention in Human Crowds. 2018 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2018.8460504
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