July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Visually-Guided Collective Behavior in Human Swarms
Author Affiliations
  • Kevin W. Rio
    Dept. of Cognitive, Linguistic, and Psychological Sciences Brown University
  • William H. Warren
    Dept. of Cognitive, Linguistic, and Psychological Sciences Brown University
Journal of Vision July 2013, Vol.13, 481. doi:10.1167/13.9.481
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      Kevin W. Rio, William H. Warren; Visually-Guided Collective Behavior in Human Swarms. Journal of Vision 2013;13(9):481. doi: 10.1167/13.9.481.

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

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Abstract

Human crowds, like bird flocks and fish schools, display remarkable patterns of collective locomotion. Do these global patterns emerge from local visually-guided interactions between neighbors? Here we address this question by quantifying the coordination in position and heading between neighbors in a crowd. We collected naturalistic data from small "swarms" of 20 participants who walked in an open 12x20 m area for 2 min trials. Participants were instructed to randomly veer left or right while staying together as a group, remaining within bounds. There was no designated leader and no signal to turn. Head positions were tracked at 60 Hz using a 16-camera infrared motion capture system (Qualisys Oqus 500). We computed the difference in heading between each pedestrian in the interior of the crowd and each of their N nearest neighbors. The mean heading difference was small (M = -1.5°, SD = 23.1°) across all pairs of pedestrians, but the SD increased linearly with N. This indicates a strong local coupling of alignment in heading. For each pedestrian, we also computed the bearing direction of each neighbor relative to the crowd’s mean heading. Neighbors are more likely to be found along the axis of travel (in front, behind) than perpendicular to it (alongside). This bias is consistent with observations of surf scoters swimming on the water’s surface (Lukeman et al., 2010), but orthogonal to observations of flying starlings (Cavagna et al., 2010). The results suggest that pedestrians interact locally to align their heading and walk behind their nearest neighbors. This analysis of the local coupling field can be combined with models of pedestrian interactions, such as following (Rio & Warren, PED 2012), side-by-side walking (Page & Warren, VSS 2013), and heading alignment (Bonneaud & Warren, PED 2012) to simulate human crowd dynamics (Warren, Kiefer, & Bonneaud, VSS 2013).

Meeting abstract presented at VSS 2013

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