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Video-Based Analysis and Reporting of Riding Behavior in Cyclocross Segments.

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This summary is machine-generated.

This study introduces a video analysis pipeline for cyclocross, extracting rider lines and behavior. This method offers valuable insights for performance analysis and race summarization.

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Area of Science:

  • Sports Science
  • Computer Vision
  • Biomechanics

Background:

  • Video-based trajectory analysis is common in sports like soccer and basketball.
  • Its application in cycling, particularly cyclocross, is less explored.

Purpose of the Study:

  • To present a video processing pipeline for extracting riding lines in cyclocross races.
  • To enable detailed analysis of rider behavior and performance metrics.

Main Methods:

  • Utilized an Alphapose skeleton detector and a spatiotemporally aware pose tracker to identify and track riders.
  • Enriched pose data with meta-information like rider posture (sitting/standing) and team affiliation.
  • Developed a post-processor to consolidate information and propose rider lines with associated meta-data.

Main Results:

  • Successfully extracted riding lines and rider behavior from video feeds.
  • Enabled insights into intra-athlete ride line clustering and anomaly detection.
  • Provided detailed breakdowns of riding and running durations within race segments.

Conclusions:

  • The developed methodology offers valuable data for performance analysis in cyclocross.
  • Insights can enhance sports storytelling and facilitate automatic race summarization.