# TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications

arXiv: https://arxiv.org/abs/1907.03698 · HTML: https://mlsanity.com/paper/1907.03698/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 30 of 33)

- [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)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps](https://mlsanity.com/paper/2409.14543/) (2024)
- [Widely Applicable Strong Baseline for Sports Ball Detection and Tracking](https://mlsanity.com/paper/2311.05237/) (2023)
- [ViSTec: Video Modeling for Sports Technique Recognition and Tactical Analysis](https://mlsanity.com/paper/2402.15952/) (2024)
- [Estimation of control area in badminton doubles with pose information from top and back view drone videos](https://mlsanity.com/paper/2305.04247/) (2023)
- [Pose2Trajectory: Using Transformers on Body Pose to Predict Tennis Player's Trajectory](https://mlsanity.com/paper/2411.04501/) (2024)
- [TT3D: Table Tennis 3D Reconstruction](https://mlsanity.com/paper/2504.10035/) (2025)
- [EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos](https://mlsanity.com/paper/2101.04954/) (2021)
- [Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 Basketball](https://mlsanity.com/paper/2412.06258/) (2024)
- [Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges](https://mlsanity.com/paper/2505.03991/) (2025)
- [Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation](https://mlsanity.com/paper/2511.20250/) (2025)
- [Classification of Tennis Actions Using Deep Learning](https://mlsanity.com/paper/2402.02545/) (2024)
- [BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports](https://mlsanity.com/paper/2502.21085/) (2025)
- [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)
- [Heatmap-based Object Detection and Tracking with a Fully Convolutional Neural Network](https://mlsanity.com/paper/2101.03541/) (2021)
- [maskGRU: Tracking Small Objects in the Presence of Large Background Motions](https://mlsanity.com/paper/2201.00467/) (2022)
- [BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking](https://mlsanity.com/paper/2509.18387/) (2025)
- [An Analysis of Kalman Filter based Object Tracking Methods for Fast-Moving Tiny Objects](https://mlsanity.com/paper/2509.18451/) (2025)
- [BFMD: A Full-Match Badminton Dense Dataset for Dense Shot Captioning](https://mlsanity.com/paper/2603.25533/) (2026)
- [TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos](https://mlsanity.com/paper/2605.01234/) (2026)
- [A Badminton Recognition and Tracking System Based on Context Multi-feature Fusion](https://mlsanity.com/paper/2306.14492/) (2023)
- [An All Deep System for Badminton Game Analysis](https://mlsanity.com/paper/2308.12645/) (2023)
- [Physics-Guided Fusion for Robust 3D Tracking of Fast Moving Small Objects](https://mlsanity.com/paper/2510.20126/) (2025)
- [TrackNetV5: Residual-Driven Spatio-Temporal Refinement and Motion Direction Decoupling for Fast Object Tracking](https://mlsanity.com/paper/2512.02789/) (2025)
- [One-Shot Badminton Shuttle Detection for Mobile Robots](https://mlsanity.com/paper/2603.06691/) (2026)
- [Multi-Task Tennis Stroke Biomechanics Analysis Using MediaPipe Pose](https://mlsanity.com/paper/2606.15992/) (2026)
- [DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton](https://mlsanity.com/paper/2608.24439/) (2026)

## Abstract (arXiv)

Ball trajectory data are one of the most fundamental and useful information in the evaluation of players' performance and analysis of game strategies. Although vision-based object tracking techniques have been developed to analyze sport competition videos, it is still challenging to recognize and position a high-speed and tiny ball accurately. In this paper, we develop a deep learning network, called TrackNet, to track the tennis ball from broadcast videos in which the ball images are small, blurry, and sometimes with afterimage tracks or even invisible. The proposed heatmap-based deep learning network is trained to not only recognize the ball image from a single frame but also learn flying patterns from consecutive frames. TrackNet takes images with a size of 640×360 to generate a detection heatmap from either a single frame or several consecutive frames to position the ball and can achieve high precision even on public domain videos. The network is evaluated on the video of the men's singles final at the 2017 Summer Universiade, which is available on YouTube. The precision, recall, and F1-measure of TrackNet reach 99.7%, 97.3%, and 98.5%, respectively. To prevent overfitting, 9 additional videos are partially labeled together with a subset from the previous dataset to implement 10-fold cross-validation, and the precision, recall, and F1-measure are 95.3%, 75.7%, and 84.3%, respectively. A conventional image processing algorithm is also implemented to compare with TrackNet. Our experiments indicate that TrackNet outperforms conventional method by a big margin and achieves exceptional ball tracking performance. The dataset and demo video are available at https://nol.cs.nctu.edu.tw/ndo3je6av9/.