September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Recruitment of Pedestrians into Collective Crowd Motion
Author Affiliations
  • Trenton Wirth
    Brown University
  • William Warren
    Brown University
Journal of Vision August 2017, Vol.17, 717. doi:10.1167/17.10.717
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      Trenton Wirth, William Warren; Recruitment of Pedestrians into Collective Crowd Motion. Journal of Vision 2017;17(10):717. doi: 10.1167/17.10.717.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Collective motion in human crowds is thought to emerge from visual interactions between individual pedestrians. A key problem in understanding the formation of collective motion is the conditions under which an individual is recruited into the collective and aligns their heading direction with their neighbors. In previous research, we found that a participant is influenced by a weighted average of visible neighbors, with a weight that decays linearly with distance (Warren & Rio, VSS 2015; Wirth & Warren, VSS 2016). Thus, a greater alignment of neighbors within this neighborhood should have a stronger influence on the participant's heading direction. Participants walked in a 12 by 14 meter tracking space, among a virtual crowd while wearing a wireless Oculus head-mounted display; head trajectory was recorded. A crowd of 24 virtual humans appeared within a 176° horizontal window in front of the participant, evenly distributed in depth from 2.5 to 8.5 meters. During a trial, the crowd walked forward for one second, and then the entire crowd would turn either 10° or 20° to the left or right. Noise was added into individual heading directions, randomly selected from a square distribution with a range of +/- 15°, +/- 30°, or +/- 45° about the common turn angle. The results show that the participants' mean final heading direction was close to 10° and 20°, while the within-subject SD significantly increased with crowd noise (p< .01). We plan to simulate these results using the neighborhood model. The findings are consistent with stronger recruitment of individual pedestrians by neighbors who are more aligned.

Meeting abstract presented at VSS 2017

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