# Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings

arXiv: https://arxiv.org/abs/2004.09776 · HTML: https://mlsanity.com/paper/2004.09776/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer](https://mlsanity.com/paper/2504.19863/) (2025)
- [Creating Multimedia Summaries Using Tweets and Videos](https://mlsanity.com/paper/2203.08931/) (2022)
- [Event Detection in Table Tennis Videos using 2D Keypoints](https://mlsanity.com/paper/2610.08286/) (2026)

## Abstract (arXiv)

In this paper we address the problem of motion event detection in athlete recordings from individual sports. In contrast to recent end-to-end approaches, we propose to use 2D human pose sequences as an intermediate representation that decouples human motion from the raw video information. Combined with domain-adapted athlete tracking, we describe two approaches to event detection on pose sequences and evaluate them in complementary domains: swimming and athletics. For swimming, we show how robust decision rules on pose statistics can detect different motion events during swim starts, with a F1 score of over 91% despite limited data. For athletics, we use a convolutional sequence model to infer stride-related events in long and triple jump recordings, leading to highly accurate detections with 96% in F1 score at only +/- 5ms temporal deviation. Our approach is not limited to these domains and shows the flexibility of pose-based motion event detection.