# BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking

arXiv: https://arxiv.org/abs/2509.18387 · HTML: https://mlsanity.com/paper/2509.18387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 48 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection](https://mlsanity.com/paper/2011.12885/) (2020)
- [TTNet: Real-time temporal and spatial video analysis of table tennis](https://mlsanity.com/paper/2004.09927/) (2020)
- [Global Context Networks](https://mlsanity.com/paper/2012.13375/) (2020)
- [TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications](https://mlsanity.com/paper/1907.03698/) (2019)
- [Achieving Human Level Competitive Robot Table Tennis](https://mlsanity.com/paper/2408.03906/) (2024)
- [MonoTrack: Shuttle trajectory reconstruction from monocular badminton video](https://mlsanity.com/paper/2204.01899/) (2022)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [The World of Fast Moving Objects](https://mlsanity.com/paper/1611.07889/) (2016)
- [DeepBall: Deep Neural-Network Ball Detector](https://mlsanity.com/paper/1902.07304/) (2019)
- [Intra-frame Object Tracking by Deblatting](https://mlsanity.com/paper/1905.03633/) (2019)
- [TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps](https://mlsanity.com/paper/2409.14543/) (2024)
- [Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects](https://mlsanity.com/paper/1911.10927/) (2019)
- [Widely Applicable Strong Baseline for Sports Ball Detection and Tracking](https://mlsanity.com/paper/2311.05237/) (2023)
- [Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup](https://mlsanity.com/paper/2007.11876/) (2020)
- [FMODetect: Robust Detection of Fast Moving Objects](https://mlsanity.com/paper/2012.08216/) (2020)
- [Non-Causal Tracking by Deblatting](https://mlsanity.com/paper/1909.06894/) (2019)
- [Table Tennis Stroke Detection and Recognition Using Ball Trajectory Data](https://mlsanity.com/paper/2302.09657/) (2023)
- [LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos](https://mlsanity.com/paper/2503.20936/) (2025)
- [TT3D: Table Tennis 3D Reconstruction](https://mlsanity.com/paper/2504.10035/) (2025)
- [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)

## Built on by (most cited first, 1 of 1)

- [Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation](https://mlsanity.com/paper/2511.20250/) (2025)

## Abstract (arXiv)

Motion blur reduces the clarity of fast-moving objects, posing challenges for detection systems, especially in racket sports, where balls often appear as streaks rather than distinct points. Existing labeling conventions mark the ball at the leading edge of the blur, introducing asymmetry and ignoring valuable motion cues correlated with velocity. This paper introduces a new labeling strategy that places the ball at the center of the blur streak and explicitly annotates blur attributes. Using this convention, we release a new table tennis ball detection dataset. We demonstrate that this labeling approach consistently enhances detection performance across various models. Furthermore, we introduce BlurBall, a model that jointly estimates ball position and motion blur attributes. By incorporating attention mechanisms such as Squeeze-and-Excitation over multi-frame inputs, we achieve state-of-the-art results in ball detection. Leveraging blur not only improves detection accuracy but also enables more reliable trajectory prediction, benefiting real-time sports analytics.