# Accurate Tennis Court Line Detection on Amateur Recorded Matches

arXiv: https://arxiv.org/abs/2404.06977 · HTML: https://mlsanity.com/paper/2404.06977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [ShadowFormer: Global Context Helps Image Shadow Removal](https://mlsanity.com/paper/2302.01650/) (2023)
- [Optimizing Through Learned Errors for Accurate Sports Field Registration](https://mlsanity.com/paper/1909.08034/) (2019)

## Abstract (arXiv)

Typically, tennis court line detection is done by running Hough-Line-Detection to find straight lines in the image, and then computing a transformation matrix from the detected lines to create the final court structure. We propose numerous improvements and enhancements to this algorithm, including using pretrained State-of-the-Art shadow-removal and object-detection ML models to make our line-detection more robust. Compared to the original algorithm, our method can accurately detect lines on amateur, dirty courts. When combined with a robust ball-tracking system, our method will enable accurate, automatic refereeing for amateur and professional tennis matches alike.