# CAS-FD: Contact-Aware Temporal Sampling for Single-View Foul vs Dive Recognition

arXiv: https://arxiv.org/abs/2608.17060 · HTML: https://mlsanity.com/paper/2608.17060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 24 of 24)

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- [EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens](https://mlsanity.com/paper/2211.10636/) (2022)
- [Towards AI-Powered Video Assistant Referee System (VARS) for Association Football](https://mlsanity.com/paper/2407.12483/) (2024)
- [Real Time Offside Detection using a Single Camera in Soccer](https://mlsanity.com/paper/2502.16030/) (2025)

## Abstract (arXiv)

Distinguishing a genuine foul from a simulated dive in football remains one of the sport's most contested fine-grained recognition problems, especially when such decisions have to be from a single broadcast view without multi-view camera angle. We introduce a balanced 600-clip single-view Foul/Dive dataset and show that contact-aware sampling concentrating the model's attention around the moment of physical contact rather than treating all frames equally yields substantially improved recognition of this contact- specific problem. The proposed approach achieves 86.0% accuracy and macro-F1 0.860 on the held-out test split, a 12 percentage- point gain over contact-unaware alternatives that grows further on unseen data. We also evaluate each pipeline component against human annotations, establishing where and why the system suc- ceeds and fails. The result is a documented dataset, a reproducible single-view pipeline, and a grounded evaluation framework for fine-grained contact-event recognition in broadcast football footage. The dataset and code are available at https://github.com/hossain- tamim/contact-aware-dive.