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Continuous football player tracking from discrete broadcast data

arXiv 2311.14642 ↗First posted 24 Nov 2023cs.CV cs.MAcited by 1

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Abstract (arXiv)

Player tracking data remains out of reach for many professional football teams as their video feeds are not sufficiently high quality for computer vision technologies to be used. To help bridge this gap, we present a method that can estimate continuous full-pitch tracking data from discrete data made from broadcast footage. Such data could be collected by clubs or players at a similar cost to event data, which is widely available down to semi-professional level. We test our method using open-source tracking data, and include a version that can be applied to a large set of over 200 games with such discrete data.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 18 references that are in the map. First posted on arXiv 24 Nov 2023.

Builds on

3 of 18 references.

References · 3

  1. 2020Multi-Modal Trajectory Prediction of NBA Playerscited by 18
  2. 2022Automatic event detection in football using tracking datacited by 10
  3. 2023Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observationscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.