Pose estimates from online videos show that side-by-side walkers synchronize movement under naturalistic conditions.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2019
2019
Historique:
received:
14
12
2018
accepted:
20
05
2019
entrez:
7
6
2019
pubmed:
7
6
2019
medline:
12
2
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31170214
doi: 10.1371/journal.pone.0217861
pii: PONE-D-18-34216
pmc: PMC6553729
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0217861Subventions
Organisme : NINDS NIH HHS
ID : R01 NS063399
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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