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Who are my neighbors?: A perception model for selecting neighbors of pedestrians in crowds
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
2018 (English)In: Proceedings of the 18th International Conference on Intelligent Virtual Agents, IVA 2018, Association for Computing Machinery (ACM), 2018, p. 269-274Conference paper, Published paper (Refereed)
Abstract [en]

Pedestrian trajectory prediction is a challenging problem. One of the aspects that makes it so challenging is the fact that the future positions of an agent are not only determined by its previous positions, but also by the interaction of the agent with its neighbors. Previous methods, like Social Attention have considered the interactions with all agents as neighbors. However, this ends up assigning high attention weights to agents who are far away from the queried agent and/or moving in the opposite direction, even though, such agents might have little to no impact on the queried agent’s trajectory. Furthermore, trajectory prediction of a queried agent involving all agents in a large crowded scenario is not efficient. In this paper, we propose a novel approach for selecting neighbors of an agent by modeling its perception as a combination of a location and a locomotion model. We demonstrate the performance of our method by comparing it with the existing state-of-the-art method on publicly available data-sets. The results show that our neighbor selection model overall improves the accuracy of trajectory prediction and enables prediction in scenarios with large numbers of agents in which other methods do not scale well.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2018. p. 269-274
Keywords [en]
Machine learning, Perception, Trajectory prediction, Virtual agents
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-241486DOI: 10.1145/3267851.3267875Scopus ID: 2-s2.0-85058462178ISBN: 9781450360135 (print)OAI: oai:DiVA.org:kth-241486DiVA, id: diva2:1281739
Conference
18th ACM International Conference on Intelligent Virtual Agents, IVA 2018, Western Sydney University's new Parramatta City CampusSydney, Australia, 5 November 2018 through 8 November 2018
Note

QC 20190123

Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-01-23Bibliographically approved

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Saikia, Himangshu

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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