# Widely Applicable Strong Baseline for Sports Ball Detection and Tracking

arXiv: https://arxiv.org/abs/2311.05237 · HTML: https://mlsanity.com/paper/2311.05237/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 112 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection](https://mlsanity.com/paper/2011.12885/) (2020)
- [Lite-HRNet: A Lightweight High-Resolution Network](https://mlsanity.com/paper/2104.06403/) (2021)
- [Hierarchical Deep Temporal Models for Group Activity Recognition](https://mlsanity.com/paper/1607.02643/) (2016)
- [Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation](https://mlsanity.com/paper/1805.02798/) (2018)
- [Social Adaptive Module for Weakly-supervised Group Activity Recognition](https://mlsanity.com/paper/2007.09470/) (2020)
- [TTNet: Real-time temporal and spatial video analysis of table tennis](https://mlsanity.com/paper/2004.09927/) (2020)
- [TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications](https://mlsanity.com/paper/1907.03698/) (2019)
- [MonoTrack: Shuttle trajectory reconstruction from monocular badminton video](https://mlsanity.com/paper/2204.01899/) (2022)
- [Skeleton-based Relational Reasoning for Group Activity Analysis](https://mlsanity.com/paper/2011.05653/) (2020)
- [DeepBall: Deep Neural-Network Ball Detector](https://mlsanity.com/paper/1902.07304/) (2019)
- [DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes](https://mlsanity.com/paper/2112.00627/) (2021)
- [Tracking Small and Fast Moving Objects: A Benchmark](https://mlsanity.com/paper/2209.04284/) (2022)
- [Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup](https://mlsanity.com/paper/2007.11876/) (2020)
- [FootAndBall: Integrated player and ball detector](https://mlsanity.com/paper/1912.05445/) (2019)

## Built on by (most cited first, 9 of 9)

- [TT3D: Table Tennis 3D Reconstruction](https://mlsanity.com/paper/2504.10035/) (2025)
- [Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation](https://mlsanity.com/paper/2511.20250/) (2025)
- [TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos](https://mlsanity.com/paper/2508.09650/) (2025)
- [YO-CSA-T: A Real-time Badminton Tracking System Utilizing YOLO Based on Contextual and Spatial Attention](https://mlsanity.com/paper/2501.06472/) (2025)
- [BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking](https://mlsanity.com/paper/2509.18387/) (2025)
- [Ice Hockey Puck Localization Using Contextual Cues](https://mlsanity.com/paper/2506.04365/) (2025)
- [One-Shot Badminton Shuttle Detection for Mobile Robots](https://mlsanity.com/paper/2603.06691/) (2026)
- [Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning](https://mlsanity.com/paper/2604.04467/) (2026)
- [NBA_Streaming: A Large-Scale Benchmark for Fine-Grained Basketball Commentary Generation in Continuous Streams](https://mlsanity.com/paper/2608.09200/) (2026)

## Abstract (arXiv)

In this work, we present a novel Sports Ball Detection and Tracking (SBDT) method that can be applied to various sports categories. Our approach is composed of (1) high-resolution feature extraction, (2) position-aware model training, and (3) inference considering temporal consistency, all of which are put together as a new SBDT baseline. Besides, to validate the wide-applicability of our approach, we compare our baseline with 6 state-of-the-art SBDT methods on 5 datasets from different sports categories. We achieve this by newly introducing two SBDT datasets, providing new ball annotations for two datasets, and re-implementing all the methods to ease extensive comparison. Experimental results demonstrate that our approach is substantially superior to existing methods on all the sports categories covered by the datasets. We believe our proposed method can play as a Widely Applicable Strong Baseline (WASB) of SBDT, and our datasets and codebase will promote future SBDT research. Datasets and codes are available at https://github.com/nttcom/WASB-SBDT .