Summary of A Simple and Effective Temporal Grounding Pipeline For Basketball Broadcast Footage, by Levi Harris
A Simple and Effective Temporal Grounding Pipeline for Basketball Broadcast Footage
by Levi Harris
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a reliable temporal grounding pipeline for video-to-analytic alignment of basketball broadcast footage, enabling quick extraction of time-remaining and quarter values from scenes. The method aims to expedite the development of multi-modal video datasets for training data-hungry video models in sports action recognition. The approach aligns pre-labeled play-by-play annotations with video frames, allowing for rapid retrieval of labeled segments. Unlike previous methods, it doesn’t require localizing game clocks by fine-tuning an object detector; instead, it directly finds semantic text regions. The end-to-end pipeline improves generality and can be deployed in a large computing cluster using interpolation and parallelization techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to quickly understand what’s happening in basketball videos. It helps match the video frames with time information like seconds left in the game or which quarter it is. This makes it easier to create big datasets for training artificial intelligence models that can recognize sports actions. The method doesn’t need special clock-locating technology; instead, it finds important text regions directly. The result is a better way to understand videos and prepare them for use with AI. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Grounding » Multi modal
