An Offline Neuro-Symbolic Football Pattern Retrieval Approach Using Constraint Programming
(2026) 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026) — Vol. 379, p. 16:1-16:22 (2026)
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- 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.
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Citations
Crespin, A., & Schaus, P. (2026). An Offline Neuro-Symbolic Football Pattern Retrieval Approach Using Constraint Programming. 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026), 379, 16:1-16:22. https://doi.org/10.4230/LIPIcs.CP.2026.16 (Original work published 2026)