事件检测器能离开转播镜头吗?来自网球的证据
Can Event Detectors Leave the Broadcast Camera? Evidence from Tennis
AI 导读
arXiv 论文发布网球弹跳与击球事件的帧级基准,包含 345 条标注、8 段混合转播、高位固定、手机和场地视角的片段,并提供混淆感知匹配器与冻结测试集。
正文 · 原文
Abstract:Ball bounces and racket hits are the events from which tennis statistics are derived, and on monocular video the two are easy to confuse. We release a benchmark of 345 bounce and hit annotations at frame level on eight clips that mix broadcast, fixed elevated, phone and court-level viewpoints, with a frozen development and test split, a confusion-aware matcher, clip-level bootstrap intervals and a blind second annotation of three clips. On it we score supervised precise spotters with published broadcast weights, two zero-shot vision-language models, and RallyArb, an arbitration layer over a ball track and player poses that fits no parameter on event labels. The supervised spotters score near-perfectly on broadcast footage and almost nothing on phone and court-level footage, where the pooled bounce F1 of E2E-Spot is 0.130. RallyArb keeps a bounce F1 above 0.8 on the three phone and court-level clips, two of which were used to tune it, and finds 8 of 11 bounces on the held-out one, but it places hits too far from the contact frame to match human timing. The vision-language models detect events on every viewpoint and miss the contact frame by about 0.1 s. Fine-tuned leave-one-clip-out from its broadcast weights, the spotter reaches about 0.8 bounce F1 on two phone and court-level clips and stays at 0.2 on a grazing phone angle. The annotations, the evaluator and the frozen predictions of every scored row are released.
| Comments: | 16 pages, 4 figures, 5 tables. Benchmark, evaluator and frozen predictions: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.38042 [cs.CV] |
| (or arXiv:2610.38042v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38042 arXiv-issued DOI via DataCite |
Submission history
From: André-Louis Rochet [view email]
[v1]
Tue, 27 Oct 2026 22:54:45 UTC (676 KB)
来源:arXiv cs.AI · arxiv.org