# Learning Football Body-Orientation as a Matter of Classification

arXiv: https://arxiv.org/abs/2106.00359 · HTML: https://mlsanity.com/paper/2106.00359/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [A Context-Aware Loss Function for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1912.01326/) (2019)
- [Using Player's Body-Orientation to Model Pass Feasibility in Soccer](https://mlsanity.com/paper/2004.07209/) (2020)
- [Real-Time Camera Pose Estimation for Sports Fields](https://mlsanity.com/paper/2003.14109/) (2020)
- [Group Activity Detection from Trajectory and Video Data in Soccer](https://mlsanity.com/paper/2004.10299/) (2020)
- [Player Chemistry: Striving for a Perfectly Balanced Soccer Team](https://mlsanity.com/paper/2003.01712/) (2020)
- [Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players](https://mlsanity.com/paper/2003.00943/) (2020)

## Built on by (most cited first, 2 of 2)

- [Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage](https://mlsanity.com/paper/2202.12259/) (2022)
- [Player Pressure Map -- A Novel Representation of Pressure in Soccer for Evaluating Player Performance in Different Game Contexts](https://mlsanity.com/paper/2401.16235/) (2024)

## Abstract (arXiv)

Orientation is a crucial skill for football players that becomes a differential factor in a large set of events, especially the ones involving passes. However, existing orientation estimation methods, which are based on computer-vision techniques, still have a lot of room for improvement. To the best of our knowledge, this article presents the first deep learning model for estimating orientation directly from video footage. By approaching this challenge as a classification problem where classes correspond to orientation bins, and by introducing a cyclic loss function, a well-known convolutional network is refined to provide player orientation data. The model is trained by using ground-truth orientation data obtained from wearable EPTS devices, which are individually compensated with respect to the perceived orientation in the current frame. The obtained results outperform previous methods; in particular, the absolute median error is less than 12 degrees per player. An ablation study is included in order to show the potential generalization to any kind of football video footage.