# Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors

arXiv: https://arxiv.org/abs/2205.10450 · HTML: https://mlsanity.com/paper/2205.10450/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations](https://mlsanity.com/paper/2106.01548/) (2021)
- [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)
- [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)
- [Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts](https://mlsanity.com/paper/2104.06779/) (2021)
- [Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection](https://mlsanity.com/paper/2106.14447/) (2021)
- [Improved Soccer Action Spotting using both Audio and Video Streams](https://mlsanity.com/paper/2011.04258/) (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)
- [Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network](https://mlsanity.com/paper/2204.02674/) (2022)

## Built on by (most cited first, 27 of 27)

- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [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)
- [SoccerNet 2022 Challenges Results](https://mlsanity.com/paper/2210.02365/) (2022)
- [X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model](https://mlsanity.com/paper/2404.06332/) (2024)
- [T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos](https://mlsanity.com/paper/2404.05392/) (2024)
- [SoccerNet 2023 Challenges Results](https://mlsanity.com/paper/2309.06006/) (2023)
- [COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers](https://mlsanity.com/paper/2309.01270/) (2023)
- [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)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Action Anticipation from SoccerNet Football Video Broadcasts](https://mlsanity.com/paper/2504.12021/) (2025)
- [Unifying Global and Local Scene Entities Modelling for Precise Action Spotting](https://mlsanity.com/paper/2404.09951/) (2024)
- [Towards AI-Powered Video Assistant Referee System (VARS) for Association Football](https://mlsanity.com/paper/2407.12483/) (2024)
- [SoccerNet 2024 Challenges Results](https://mlsanity.com/paper/2409.10587/) (2024)
- [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)
- [Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance](https://mlsanity.com/paper/2503.00147/) (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)
- [AdaSpot: Spend Resolution Where It Matters for Precise Event Spotting](https://mlsanity.com/paper/2602.22073/) (2026)
- [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)
- [Entity-Aware Sequence Transduction for Player-Centric Ball Action Spotting](https://mlsanity.com/paper/2608.01696/) (2026)

## Abstract (arXiv)

We present a model for temporally precise action spotting in videos, which uses a dense set of detection anchors, predicting a detection confidence and corresponding fine-grained temporal displacement for each anchor. We experiment with two trunk architectures, both of which are able to incorporate large temporal contexts while preserving the smaller-scale features required for precise localization: a one-dimensional version of a u-net, and a Transformer encoder (TE). We also suggest best practices for training models of this kind, by applying Sharpness-Aware Minimization (SAM) and mixup data augmentation. We achieve a new state-of-the-art on SoccerNet-v2, the largest soccer video dataset of its kind, with marked improvements in temporal localization. Additionally, our ablations show: the importance of predicting the temporal displacements; the trade-offs between the u-net and TE trunks; and the benefits of training with SAM and mixup.