# Improved Soccer Action Spotting using both Audio and Video Streams

arXiv: https://arxiv.org/abs/2011.04258 · HTML: https://mlsanity.com/paper/2011.04258/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 21 of 21)

- [Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting](https://mlsanity.com/paper/2104.09333/) (2021)
- [Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts](https://mlsanity.com/paper/2104.06779/) (2021)
- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection](https://mlsanity.com/paper/2106.14447/) (2021)
- [Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors](https://mlsanity.com/paper/2205.10450/) (2022)
- [RMS-Net: Regression and Masking for Soccer Event Spotting](https://mlsanity.com/paper/2102.07624/) (2021)
- [ASTRA: An Action Spotting TRAnsformer for Soccer Videos](https://mlsanity.com/paper/2404.01891/) (2024)
- [Action Spotting using Dense Detection Anchors Revisited: Submission to the SoccerNet Challenge 2022](https://mlsanity.com/paper/2206.07846/) (2022)
- [Towards Active Learning for Action Spotting in Association Football Videos](https://mlsanity.com/paper/2304.04220/) (2023)
- [SoccerNet-Echoes: A Soccer Game Audio Commentary Dataset](https://mlsanity.com/paper/2405.07354/) (2024)
- [A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification](https://mlsanity.com/paper/2211.12334/) (2022)
- [Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives](https://mlsanity.com/paper/2309.12067/) (2023)
- [Deep learning for action spotting in association football videos](https://mlsanity.com/paper/2410.01304/) (2024)
- [A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games](https://mlsanity.com/paper/2108.11149/) (2021)
- [Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation](https://mlsanity.com/paper/2202.05728/) (2022)
- [Automated Detection of Sport Highlights from Audio and Video Sources](https://mlsanity.com/paper/2501.16100/) (2025)
- [Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges](https://mlsanity.com/paper/2505.03991/) (2025)
- [OSL-ActionSpotting: A Unified Library for Action Spotting in Sports Videos](https://mlsanity.com/paper/2407.01265/) (2024)
- [SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection](https://mlsanity.com/paper/2501.09281/) (2025)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [Multi-Focus Temporal Shifting for Precise Event Spotting in Sports Videos](https://mlsanity.com/paper/2507.07381/) (2025)

## Abstract (arXiv)

In this paper, we propose a study on multi-modal (audio and video) action spotting and classification in soccer videos. Action spotting and classification are the tasks that consist in finding the temporal anchors of events in a video and determine which event they are. This is an important application of general activity understanding. Here, we propose an experimental study on combining audio and video information at different stages of deep neural network architectures. We used the SoccerNet benchmark dataset, which contains annotated events for 500 soccer game videos from the Big Five European leagues. Through this work, we evaluated several ways to integrate audio stream into video-only-based architectures. We observed an average absolute improvement of the mean Average Precision (mAP) metric of 7.43% for the action classification task and of 4.19% for the action spotting task.