CAS-FD: Contact-Aware Temporal Sampling for Single-View Foul vs Dive Recognition
arXiv:2608.17060v1 Announce Type: new Abstract: 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.
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[Submitted on 17 Aug 2026]
Title:CAS-FD: Contact-Aware Temporal Sampling for Single-View Foul vs Dive Recognition
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Abstract: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 this https URL tamim/contact-aware-dive.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.17060 [cs.CV]
(or arXiv:2608.17060v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.17060
arXiv-issued DOI via DataCite (pending registration)
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From: Md Tamim Hossain [view email] [v1] Mon, 17 Aug 2026 19:06:42 UTC (9,549 KB)
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