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crespina/TactiCP

Authors: Augustin Crespin

Abstract

Cite as

@misc{dagpub-supp--paper-25265-urlgithub.com-crespina-TactiCP,
   title = {{crespina/TactiCP}}, 
   author = {Crespin, Augustin},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:ebbdc79a85424a95910e726f83f2c17a2cbcf297;origin=https://github.com/crespina/TactiCP;visit=swh:1:snp:f48732c6fe88f51b80198d3f08029cc9acab288c;anchor=swh:1:rev:380f87ffb7b0a6f09069a390263181700128a014}{\texttt{swh:1:dir:ebbdc79a85424a95910e726f83f2c17a2cbcf297}} (visited on 2026-07-13)},
   url = {https://github.com/crespina/TactiCP},
}

Document

DOI: 10.4230/LIPIcs.CP.2026.16

An Offline Neuro-Symbolic Football Pattern Retrieval Approach Using Constraint Programming

Authors: Augustin Crespin and Pierre Schaus

Published in: LIPIcs, Volume 379, 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)

Abstract

Using a single broadcast camera, modern deep learning methods can detect and label players and ball positions on a frame-by-frame basis. This work focuses on post-game analysis, where frame-level labels are available for the entire video sequence. Deep learning alone performs poorly when retrieving intervals of frames in which specific spatio-temporal conditions or tactical patterns occur involving players and ball positions. A loosely coupled neuro-symbolic approach is proposed, in which these precomputed frame-level detections are processed through an SQL-like domain-specific query language. Each query is compiled into a Constraint Programming (CP) model that retrieves intervals of frames satisfying the specified constraints. The method leverages well-established CP constructs, such as time intervals and regular constraints. Experiments on real football games demonstrate that this approach is simple and efficient, enabling expressive querying for post-game tactical analysis while remaining accurate and scalable.

Cite as

Augustin Crespin and Pierre Schaus. An Offline Neuro-Symbolic Football Pattern Retrieval Approach Using Constraint Programming. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 16:1-16:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{crespin_et_al:LIPIcs.CP.2026.16,
  author =	{Crespin, Augustin and Schaus, Pierre},
  title =	{{An Offline Neuro-Symbolic Football Pattern Retrieval Approach Using Constraint Programming}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{16:1--16:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-432-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{379},
  editor =	{Beldiceanu, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2026.16},
  URN =		{urn:nbn:de:0030-drops-266491},
  doi =		{10.4230/LIPIcs.CP.2026.16},
  annote =	{Keywords: Pattern Matching, Domain-Specific Language, Video Analysis}
}

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Documents authored by Crespin, Augustin

crespina/TactiCP

Abstract

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An Offline Neuro-Symbolic Football Pattern Retrieval Approach Using Constraint Programming

Abstract

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