# DeepBall: Deep Neural-Network Ball Detector

arXiv: https://arxiv.org/abs/1902.07304 · HTML: https://mlsanity.com/paper/1902.07304/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Widely Applicable Strong Baseline for Sports Ball Detection and Tracking](https://mlsanity.com/paper/2311.05237/) (2023)
- [Puck localization and multi-task event recognition in broadcast hockey videos](https://mlsanity.com/paper/2105.10563/) (2021)
- [TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos](https://mlsanity.com/paper/2508.09650/) (2025)
- [RacketVision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysis](https://mlsanity.com/paper/2511.17045/) (2025)
- [BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking](https://mlsanity.com/paper/2509.18387/) (2025)
- [Multi-Modal Soccer Scene Analysis with Masked Pre-Training](https://mlsanity.com/paper/2512.19528/) (2025)
- [TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos](https://mlsanity.com/paper/2605.01234/) (2026)
- [Monocular Visual Analysis for Electronic Line Calling of Tennis Games](https://mlsanity.com/paper/2107.09255/) (2021)
- [Event Detection in Football using Graph Convolutional Networks](https://mlsanity.com/paper/2301.10052/) (2023)
- [BMASH: Ball-Motion-Aware Soccer Header Spotting](https://mlsanity.com/paper/2609.39300/) (2026)

## Abstract (arXiv)

The paper describes a deep network based object detector specialized for ball detection in long shot videos. Due to its fully convolutional design, the method operates on images of any size and produces ball confidence map encoding the position of detected ball. The network uses hypercolumn concept, where feature maps from different hierarchy levels of the deep convolutional network are combined and jointly fed to the convolutional classification layer. This allows boosting the detection accuracy as larger visual context around the object of interest is taken into account. The method achieves state-of-the-art results when tested on publicly available ISSIA-CNR Soccer Dataset.