自动提取篮球直播视频的剩余时间与节次信息，助力体育动作识别数据构建
A Simple and Effective Temporal Grounding Pipeline for Basketball Broadcast Footage
- 直接识别画面中的时间与节次文本，无需微调检测器
- 实现高精度帧级对齐，支持快速检索标注片段
- 端到端设计适合大规模视频数据处理，开源可复用
我们提出一种可靠的时序定位流水线，用于篮球直播视频与赛事分析数据的对齐。给定一系列视频帧，该方法能快速准确地从比赛画面中提取剩余时间与节次信息。本工作旨在加速构建大规模多模态视频数据集，以训练体育动作识别中数据密集型模型。通过将预标注的逐球记录（play-by-play）注释语料库与视频帧对齐，实现标签化视频段的快速检索。与以往方法不同，本方案不依赖微调现成目标检测器来定位比赛计时器，而是直接识别语义文本区域，具备更强泛化能力。此外，采用插值与并行化技术，使该流水线可部署于大型计算集群。所有代码已公开。
原文摘要 · Abstract (English)
We present a reliable temporal grounding pipeline for video-to-analytic alignment of basketball broadcast footage. Given a series of frames as input, our method quickly and accurately extracts time-remaining and quarter values from basketball broadcast scenes. Our work intends to expedite the development of large, multi-modal video datasets to train data-hungry video models in the sports action recognition domain. Our method aligns a pre-labeled corpus of play-by-play annotations containing dense event annotations to video frames, enabling quick retrieval of labeled video segments. Unlike previous methods, we forgo the need to localize game clocks by fine-tuning an out-of-the-box object detector to find semantic text regions directly. Our end-to-end approach improves the generality of our work. Additionally, interpolation and parallelization techniques prepare our pipeline for deployment in a large computing cluster. All code is made publicly available.
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