Learning pedestrian dynamics from the real world
Open accessAbstract
In this paper we describe a method to learn parameters which govern pedestrian motion by observing video data. Our learning framework is based on variational mode learning and allows us to efficiently optimize a continuous pedestrian cost model. We show that this model can be trained on automatic tracking results, and provides realistic and accurate pedestrian motions.
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References 14 · Cited by 135
- Authors
- Scovanner, P., & Tappen, M. F.
- Affiliations
- University of Central Florida (US)
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 381-388
- OpenAlex
- W2534527426
- Cited by
- 135
- References
- 14
- Citations by year
- 2025: 6 · 2024: 4 · 2023: 4 · 2022: 6 · 2021: 6 · 2020: 7 · 2019: 9 · 2018: 7 · 2017: 12 · 2016: 12 · 2015: 9 · 2014: 11 · 2013: 12 · 2012: 12
- FWCI
- 20.04
- Type
- conference paper
- Language
- en
- Open access
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Journal of International Crisis and Risk Communication Research (submittedVersion)
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Evacuation and Crowd Dynamics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- pedestrian dynamics, automatic tracking, crowd simulation
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