Pose estimates from online videos show that side-by-side walkers synchronize movement under naturalistic conditions

Claire Chambers, Gaiqing Kong, Kunlin Wei and Konrad Kording

PLOS ONE, 2019, vol. 14, issue 6, 1-17

Abstract: Marker-less video-based pose estimation promises to allow us to do movement science on existing video databases. We revisited the old question of how people synchronize their walking using real world data. We thus applied pose estimation to 348 video segments extracted from YouTube videos of people walking in cities. As in previous, more constrained, research, we find a tendency for pairs of people to walk in phase or in anti-phase with each other. Large video databases, along with pose-estimation algorithms, promise answers to many movement questions without experimentally acquiring new data.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0217861

DOI: 10.1371/journal.pone.0217861

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