# EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos

arXiv: https://arxiv.org/abs/2101.04954 · HTML: https://mlsanity.com/paper/2101.04954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 65 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [FOTS: Fast Oriented Text Spotting with a Unified Network](https://mlsanity.com/paper/1801.01671/) (2018)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Extreme clicking for efficient object annotation](https://mlsanity.com/paper/1708.02750/) (2017)
- [TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications](https://mlsanity.com/paper/1907.03698/) (2019)
- [Crowdsourcing in Computer Vision](https://mlsanity.com/paper/1611.02145/) (2016)
- [A Survey on Content-Aware Video Analysis for Sports](https://mlsanity.com/paper/1703.01170/) (2017)
- [Detecting events and key actors in multi-person videos](https://mlsanity.com/paper/1511.02917/) (2015)
- [Embracing Error to Enable Rapid Crowdsourcing](https://mlsanity.com/paper/1602.04506/) (2016)
- [PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes](https://mlsanity.com/paper/2009.02464/) (2020)

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

- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [An Evaluation of Hybrid Annotation Workflows on High-Ambiguity Spatiotemporal Video Footage](https://mlsanity.com/paper/2510.21798/) (2025)
- [ProTAL: A Drag-and-Link Video Programming Framework for Temporal Action Localization](https://mlsanity.com/paper/2505.17555/) (2025)

## Abstract (arXiv)

The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms. Our approach uses machine learning models in computer vision to help users acquire essential events from videos (e.g., serve, the ball bouncing on the court) and offers users a set of interactive tools for data annotation. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge.