Title

Peloton tracking and analysis from the 2016 Tour de France

Description

The work associated with this project is described in a manuscript entitled "How vision governs the collective behavior of cycling pelotons" by Belden et al., along with an electronic supplementary material document. We investigate properties of densely packed groups of bicycle racers, which are known as cycling pelotons. These pelotons exhibit features of collective animal behavior, including emergent behavior from inter-individual interactions. In this data set, we classify global shapes of the peloton, and identify and track individual cyclists to determine the details of network structure. We also investigate motion waves that propagate through the pelotons.

Document Type

Dataset

DCMI Type

Dataset

File Format

.MAT, .M, .FIG, .XLSX, .TIFF, .EPS, .TXT, .PNG

Viewing Instructions

MatLab or open source program required to open .mat files.

Publication Date

3-26-2019

Funder

Office of Naval Research

Publisher

Utah State University

Award Number

Office of Naval Research N00014-19-1-2059

Methodology

The raw data come from video footage of the 2016 Tour de France professional bicycle stage race, which is filmed from a helicopter to provide an overhead view of the pelotons. Video clips of interested are extracted and associated with certain metadata about the details of the race at the instant of the clip. Video clips are parsed into individual images at the time spacing corresponding to the frame rate of capture. A set of image processing algorithms is used to extract rider locations, network structure, and wave propagation behavior in a metric reference frame; these algorithms are described in "How vision governs the collective behavior of cycling pelotons" by Belden et al., and in the supplementary material document supplied with the paper.

Language

eng

Code Lists

See attached README file

Comments

data supports forthcoming article and will be released upon article publication.

Disciplines

Mechanical Engineering

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Files

README.txt (4 kB)
MD5: 93d37a3622396b1b7c22b0e0074c0f56

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