# Rule-Constrained Assignment for Cue-Ball Identification in Broadcast Snooker

arXiv: https://arxiv.org/abs/2609.23450 · HTML: https://mlsanity.com/paper/2609.23450/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 20 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Simple Cues Lead to a Strong Multi-Object Tracker](https://mlsanity.com/paper/2206.04656/) (2022)
- [TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications](https://mlsanity.com/paper/1907.03698/) (2019)
- [TVCalib: Camera Calibration for Sports Field Registration in Soccer](https://mlsanity.com/paper/2207.11709/) (2022)
- [Ball 3D Localization From A Single Calibrated Image](https://mlsanity.com/paper/2204.00003/) (2022)
- [PnLCalib: Sports Field Registration via Points and Lines Optimization](https://mlsanity.com/paper/2404.08401/) (2024)
- [TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps](https://mlsanity.com/paper/2409.14543/) (2024)
- [TT3D: Table Tennis 3D Reconstruction](https://mlsanity.com/paper/2504.10035/) (2025)
- [Billiards Sports Analytics: Datasets and Tasks](https://mlsanity.com/paper/2407.19686/) (2024)
- [pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild](https://mlsanity.com/paper/2504.12045/) (2025)
- [TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos](https://mlsanity.com/paper/2605.01234/) (2026)
- [BilliardPhys-Bench: Benchmarking Physical Reasoning and Visual Dynamics of Multimodal LLMs](https://mlsanity.com/paper/2605.30900/) (2026)

## Abstract (arXiv)

Accurate cue-ball identification is essential for metric analysis of broadcast snooker. Existing systems evaluate each candidate independently against a fixed white prototype and reject candidates above an appearance threshold. Under broadcast conditions, illumination changes can make colored balls appear white, while intrusions from players and equipment can obscure the cue ball or introduce competing candidates. We formulate cue-ball identification as a rule-constrained assignment problem that jointly assigns detected candidates to the bounded snooker inventory: one cue ball, up to 15 reds, and six colors with known spots. The cue ball is selected by the incremental cost of assigning each candidate to the white slot, and the conventional appearance test follows as the one-slot case. On 419 hand-annotated shots, our method improves identity accuracy from 88.1% to 95.5%, and from 80.5% to 95.2% on held-out venues. Within CueLift, our metric state-recovery system, the assignment expands coverage from 36.2% to 55.4% over 6,241 scorable shots. Assigning an estimate to every shot in a separate evaluation on 2,529 shots preserves this advantage.