# Fine-grained Activity Recognition in Baseball Videos

arXiv: https://arxiv.org/abs/1804.03247 · HTML: https://mlsanity.com/paper/1804.03247/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2018.

## Built on by (most cited first, 20 of 20)

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- [Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding](https://mlsanity.com/paper/2111.00629/) (2021)
- [Recent Trends in 2D Object Detection and Applications in Video Event Recognition](https://mlsanity.com/paper/2202.03206/) (2022)
- [How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs](https://mlsanity.com/paper/2203.12344/) (2022)
- [VIFSS: View-Invariant and Figure Skating-Specific Pose Representation Learning for Temporal Action Segmentation](https://mlsanity.com/paper/2508.10281/) (2025)
- [Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA](https://mlsanity.com/paper/2511.05611/) (2025)
- [Event-based Batting Impact Estimation](https://mlsanity.com/paper/2605.25656/) (2026)

## Abstract (arXiv)

In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos. We experimentally compare various recognition approaches capturing temporal structure in activity videos, by classifying segmented videos and extending those approaches to continuous videos. We also compare models on the extremely difficult task of predicting pitch speed and pitch type from broadcast baseball videos. We find that learning temporal structure is valuable for fine-grained activity recognition.