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People tracking with human motion predictions from social forces
Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany.
Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany. (AASS)ORCID iD: 0000-0003-3958-6179
Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany.
Social Robotics Lab, Department of Computer Science, University of Freiburg, Germany.
2010 (English)In: 2010 IEEE International Conference on Robotics and Automation, Proceedings, IEEE conference proceedings, 2010, p. 464-469Conference paper, Published paper (Refereed)
Abstract [en]

For many tasks in populated environments, robots need to keep track of current and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity or acceleration. But even over a short period, human behavior is more complex and influenced by factors such as the intended goal, other people, objects in the environment, and social rules. This motivates the use of more sophisticated motion models for people tracking especially since humans frequently undergo lengthy occlusion events. In this paper, we consider computational models developed in the cognitive and social science communities that describe individual and collective pedestrian dynamics for tasks such as crowd behavior analysis. In particular, we integrate a model based on a social force concept into a multi-hypothesis target tracker. We show how the refined motion predictions translate into more informed probability distributions over hypotheses and finally into a more robust tracking behavior and better occlusion handling. In experiments in indoor and outdoor environments with data from a laser range finder, the social force model leads to more accurate tracking with up to two times fewer data association errors.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2010. p. 464-469
Series
IEEE International Conference on Robotics and Automation, ISSN 1050-4729
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-71570DOI: 10.1109/ROBOT.2010.5509779ISI: 000284150004053Scopus ID: 2-s2.0-77955778271OAI: oai:DiVA.org:oru-71570DiVA, id: diva2:1280214
Conference
IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA, May 3-7, 2010
Available from: 2019-01-18 Created: 2019-01-18 Last updated: 2019-01-21Bibliographically approved

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Stork, Johannes Andreas

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf