# OSL-ActionSpotting: A Unified Library for Action Spotting in Sports Videos

arXiv: https://arxiv.org/abs/2407.01265 · HTML: https://mlsanity.com/paper/2407.01265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 47 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 29 of 29)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [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)
- [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022)
- [Gate-Shift Networks for Video Action Recognition](https://mlsanity.com/paper/1912.00381/) (2019)
- [Body Part-Based Representation Learning for Occluded Person Re-Identification](https://mlsanity.com/paper/2211.03679/) (2022)
- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [A Context-Aware Loss Function for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1912.01326/) (2019)
- [Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting](https://mlsanity.com/paper/2104.09333/) (2021)
- [Spotting Temporally Precise, Fine-Grained Events in Video](https://mlsanity.com/paper/2207.10213/) (2022)
- [VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views](https://mlsanity.com/paper/2304.04617/) (2023)
- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [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)
- [SoccerNet 2022 Challenges Results](https://mlsanity.com/paper/2210.02365/) (2022)
- [Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors](https://mlsanity.com/paper/2205.10450/) (2022)
- [Semi-Supervised Training to Improve Player and Ball Detection in Soccer](https://mlsanity.com/paper/2204.06859/) (2022)
- [MonoTrack: Shuttle trajectory reconstruction from monocular badminton video](https://mlsanity.com/paper/2204.01899/) (2022)
- [X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model](https://mlsanity.com/paper/2404.06332/) (2024)
- [Improved Soccer Action Spotting using both Audio and Video Streams](https://mlsanity.com/paper/2011.04258/) (2020)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Using Player's Body-Orientation to Model Pass Feasibility in Soccer](https://mlsanity.com/paper/2004.07209/) (2020)
- [RMS-Net: Regression and Masking for Soccer Event Spotting](https://mlsanity.com/paper/2102.07624/) (2021)
- [Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions](https://mlsanity.com/paper/2004.06172/) (2020)
- [Ball 3D Localization From A Single Calibrated Image](https://mlsanity.com/paper/2204.00003/) (2022)
- [Efficient tracking of team sport players with few game-specific annotations](https://mlsanity.com/paper/2204.04049/) (2022)
- [COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers](https://mlsanity.com/paper/2309.01270/) (2023)
- [A Universal Protocol to Benchmark Camera Calibration for Sports](https://mlsanity.com/paper/2404.09807/) (2024)
- [A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification](https://mlsanity.com/paper/2211.12334/) (2022)

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

- [Deep learning for action spotting in association football videos](https://mlsanity.com/paper/2410.01304/) (2024)
- [BMASH: Ball-Motion-Aware Soccer Header Spotting](https://mlsanity.com/paper/2609.39300/) (2026)

## Abstract (arXiv)

Action spotting is crucial in sports analytics as it enables the precise identification and categorization of pivotal moments in sports matches, providing insights that are essential for performance analysis and tactical decision-making. The fragmentation of existing methodologies, however, impedes the progression of sports analytics, necessitating a unified codebase to support the development and deployment of action spotting for video analysis. In this work, we introduce OSL-ActionSpotting, a Python library that unifies different action spotting algorithms to streamline research and applications in sports video analytics. OSL-ActionSpotting encapsulates various state-of-the-art techniques into a singular, user-friendly framework, offering standardized processes for action spotting and analysis across multiple datasets. We successfully integrated three cornerstone action spotting methods into OSL-ActionSpotting, achieving performance metrics that match those of the original, disparate codebases. This unification within a single library preserves the effectiveness of each method and enhances usability and accessibility for researchers and practitioners in sports analytics. By bridging the gaps between various action spotting techniques, OSL-ActionSpotting significantly contributes to the field of sports video analysis, fostering enhanced analytical capabilities and collaborative research opportunities. The scalable and modularized design of the library ensures its long-term relevance and adaptability to future technological advancements in the domain.