# Improving Object Detection Quality in Football Through Super-Resolution Techniques

arXiv: https://arxiv.org/abs/2402.00163 · HTML: https://mlsanity.com/paper/2402.00163/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://mlsanity.com/paper/2202.06934/) (2022)
- [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022)
- [Residual Local Feature Network for Efficient Super-Resolution](https://mlsanity.com/paper/2205.07514/) (2022)
- [NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results](https://mlsanity.com/paper/2205.05675/) (2022)
- [Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art](https://mlsanity.com/paper/2111.03260/) (2021)
- [Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM](https://mlsanity.com/paper/2306.08206/) (2023)
- [Dynamic NeRFs for Soccer Scenes](https://mlsanity.com/paper/2309.06802/) (2023)
- [Soccer Player Tracking in Low Quality Video](https://mlsanity.com/paper/2105.10700/) (2021)
- [Graph-Based Multi-Camera Soccer Player Tracker](https://mlsanity.com/paper/2211.02125/) (2022)

## Built on by (most cited first, 6 of 6)

- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model](https://mlsanity.com/paper/2404.06332/) (2024)
- [pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild](https://mlsanity.com/paper/2504.12045/) (2025)
- [Investigating Event-Based Cameras for Video Frame Interpolation in Sports](https://mlsanity.com/paper/2407.02370/) (2024)
- [Towards Athlete Fatigue Assessment from Association Football Videos](https://mlsanity.com/paper/2604.05636/) (2026)
- [SoccerNet 2026 Challenges Results](https://mlsanity.com/paper/2607.07320/) (2026)

## Abstract (arXiv)

This study explores the potential of super-resolution techniques in enhancing object detection accuracy in football. Given the sport's fast-paced nature and the critical importance of precise object (e.g. ball, player) tracking for both analysis and broadcasting, super-resolution could offer significant improvements. We investigate how advanced image processing through super-resolution impacts the accuracy and reliability of object detection algorithms in processing football match footage. Our methodology involved applying state-of-the-art super-resolution techniques to a diverse set of football match videos from SoccerNet, followed by object detection using Faster R-CNN. The performance of these algorithms, both with and without super-resolution enhancement, was rigorously evaluated in terms of detection accuracy. The results indicate a marked improvement in object detection accuracy when super-resolution preprocessing is applied. The improvement of object detection through the integration of super-resolution techniques yields significant benefits, especially for low-resolution scenarios, with a notable 12% increase in mean Average Precision (mAP) at an IoU (Intersection over Union) range of 0.50:0.95 for 320x240 size images when increasing the resolution fourfold using RLFN. As the dimensions increase, the magnitude of improvement becomes more subdued; however, a discernible improvement in the quality of detection is consistently evident. Additionally, we discuss the implications of these findings for real-time sports analytics, player tracking, and the overall viewing experience. The study contributes to the growing field of sports technology by demonstrating the practical benefits and limitations of integrating super-resolution techniques in football analytics and broadcasting.