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.. include:: ./awesome-computer-vision-in-sports.md | ||
:parser: myst_parser.sphinx_ |
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# Awesome Computer Vision in Sports | ||
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[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FAtomScott%2FSportsLabKit%2Fblob%2Fdevelop%2Fawesome-computer-vision-in-sports.md&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com) | ||
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Updated from the [original](https://github.com/avijit9/awesome-computer-vision-in-sports/blob/master/README.md#awesome-computer-vision-in-sports). See [below for full credits](#Credits). | ||
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## Papers by the year of publications | ||
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### 2022 | ||
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- SoccerNet 2022 Challenges Results, ACM MMSports'22 [[Paper]](https://arxiv.org/abs/2210.02365) | ||
- DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations, ACM MMSports'22 [[Paper]](https://arxiv.org/abs/2208.08190) | ||
- SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos CVPRW'22 [[Paper]](https://openaccess.thecvf.com/content/CVPR2022W/CVSports/papers/Scott_SoccerTrack_A_Dataset_and_Tracking_Algorithm_for_Soccer_With_Fish-Eye_CVPRW_2022_paper.pdf) | ||
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### 2021 | ||
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- LoL-V2T: Large-Scale Esports Video Description Dataset, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Tanaka_LoL-V2T_Large-Scale_Esports_Video_Description_Dataset_CVPRW_2021_paper.pdf) | ||
- Contrastive Learning for Sports Video: Unsupervised Player Classification, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Koshkina_Contrastive_Learning_for_Sports_Video_Unsupervised_Player_Classification_CVPRW_2021_paper.pdf) | ||
- Automated Tackle Injury Risk Assessment in Contact-Based Sports - A Rugby Union Example, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Martin_Automated_Tackle_Injury_Risk_Assessment_in_Contact-Based_Sports_-_A_CVPRW_2021_paper.pdf) | ||
- Toward Improving the Visual Characterization of Sport Activities With Abstracted Scene Graphs, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Rahimi_Toward_Improving_the_Visual_Characterization_of_Sport_Activities_With_Abstracted_CVPRW_2021_paper.pdf) | ||
- Detecting and Matching Related Objects With One Proposal Multiple Predictions, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Liu_Detecting_and_Matching_Related_Objects_With_One_Proposal_Multiple_Predictions_CVPRW_2021_paper.pdf) | ||
- SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Deliege_SoccerNet-v2_A_Dataset_and_Benchmarks_for_Holistic_Understanding_of_Broadcast_CVPRW_2021_paper.pdf) | ||
- Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Kulkarni_Table_Tennis_Stroke_Recognition_Using_Two-Dimensional_Human_Pose_Estimation_CVPRW_2021_paper.pdf) | ||
- Puck Localization and Multi-Task Event Recognition in Broadcast Hockey Videos, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Giancola_Temporally-Aware_Feature_Pooling_for_Action_Spotting_in_Soccer_Broadcasts_CVPRW_2021_paper.pdf) | ||
- Automatic Play Segmentation of Hockey Videos, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Pidaparthy_Automatic_Play_Segmentation_of_Hockey_Videos_CVPRW_2021_paper.pdf) | ||
- DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts Using a Single Camera, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/McNally_DeepDarts_Modeling_Keypoints_as_Objects_for_Automatic_Scorekeeping_in_Darts_CVPRW_2021_paper.pdf) | ||
- Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of Their Representations for Action Spotting, CVPRW'21 [[Paper]](https://openaccess.thecvf.com/content/CVPR2021W/CVSports/papers/Cioppa_Camera_Calibration_and_Player_Localization_in_SoccerNet-v2_and_Investigation_of_CVPRW_2021_paper.pdf) | ||
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### 2020 | ||
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- Actor-Transformers for Group Activity Recognition, CVPR'20 [[Paper]](http://isis-data.science.uva.nl/cgmsnoek/pub/gavrilyuk-transformers-cvpr2020.pdf) | ||
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- Progressive Relation Learning for Group Activity Recognition, CVPR'20 [[Paper]](https://arxiv.org/pdf/1908.02948.pdf) | ||
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- Group Activity Detection from Trajectory and Video Data in Soccer, CVPRW'20 [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w53/Sanford_Group_Activity_Detection_From_Trajectory_and_Video_Data_in_Soccer_CVPRW_2020_paper.pdf) | ||
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- FALCONS: FAst Learner-grader for CONtorted poses in Sports, CVPRW'20 [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w53/Nekoui_FALCONS_FAst_Learner-Grader_for_CONtorted_Poses_in_Sports_CVPRW_2020_paper.pdf) | ||
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- As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States Recognition, CVPRW'20 [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w53/Quiroga_As_Seen_on_TV_Automatic_Basketball_Video_Production_Using_Gaussian-Based_CVPRW_2020_paper.pdf) | ||
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- Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings, CVPRW'20 [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w53/Einfalt_Decoupling_Video_and_Human_Motion_Towards_Practical_Event_Detection_in_CVPRW_2020_paper.pdf) | ||
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- Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis, CVPRW'20 [[Paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w53/von_Braun_Utilizing_Mask_R-CNN_for_Waterline_Detection_in_Canoe_Sprint_Video_CVPRW_2020_paper.pdf) | ||
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- TTNet: Real-time temporal and spatial video analysis of table tennis, CVPRW'20 [[Paper]](https://arxiv.org/pdf/2004.09927.pdf) | ||
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### 2019 | ||
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- Learning Actor Relation Graphs for Group Activity Recognition, CVPR'19 [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Learning_Actor_Relation_Graphs_for_Group_Activity_Recognition_CVPR_2019_paper.pdf) | ||
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### 2018 | ||
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- Soccer on Your Tabletop, CVPR'18 [[Paper]](https://grail.cs.washington.edu/projects/soccer/soccer_on_your_tabletop.pdf) | ||
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- Egocentric Basketball Motion Planning From a Single First-Person Image, CVPR'18 [[Paper]](https://arxiv.org/pdf/1803.01413) | ||
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- Towards Structured Analysis of Broadcast Badminton Videos, WACV'18 [[Paper]](http://cvit.iiit.ac.in/images/ConferencePapers/2018/badminton_analytics.pdf) | ||
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- Fine-grained Activity Recognition in Baseball Videos, CVPRW'18 [[Paper]](https://arxiv.org/pdf/1804.03247.pdf) | ||
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- Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders, ECCV'18 [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Panna_Felsen_Where_Will_They_ECCV_2018_paper.pdf) | ||
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- Fine-grained Video Captioning for Sports Narrative, ECCV'18 [[Paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Fine-Grained_Video_Captioning_CVPR_2018_paper.pdf) | ||
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### 2017 | ||
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- What will Happen Next? Forecasting Player Moves in Sports Videos, ICCV'17 [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Felsen_What_Will_Happen_ICCV_2017_paper.pdf) | ||
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- Not all passes are created equal: Objectively measuring the risk and reward of passes in soccer from tracking data, SIGKDD'17 [[Paper]](https://dl.acm.org/citation.cfm?id=3098051) | ||
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- SmartTennisTV: An automatic indexing system for tennis, NCVPRIPG'17 [[Paper]](https://researchweb.iiit.ac.in/~anurag.ghosh/static/smarttennistv-automatic-indexing.pdf) | ||
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- Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition, CVPR'17 [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Bagautdinov_Social_Scene_Understanding_CVPR_2017_paper.pdf) | ||
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- Coordinated Multi-Agent Imitation Learning, ICML'17 [[Paper]](http://proceedings.mlr.press/v70/le17a/le17a.pdf) | ||
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### 2016 | ||
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- Chalkboarding: A new spatiotemporal query paradigm for sports play retrieval, ACM IUI'16 [[Paper]](https://dl.acm.org/citation.cfm?id=2856772) | ||
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- What players do with the ball: a physically constrained interaction modeling, CVPR'16 [[Paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Maksai_What_Players_Do_CVPR_2016_paper.pdf) | ||
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- Generating long-term trajectories using deep hierarchical networks, NIPS'16 [[Paper]](http://papers.nips.cc/paper/6520-generating-long-term-trajectories-using-deep-hierarchical-networks.pdf) | ||
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- Detecting Events and Key Actors in Multi-Person Videos, CVPR'16 [[Paper]](http://openaccess.thecvf.com/content_cvpr_2016/papers/Ramanathan_Detecting_Events_and_CVPR_2016_paper.pdf) | ||
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### 2015 | ||
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- Mimicking human camera operators, WACV'15 [[Paper]](https://pdfs.semanticscholar.org/e73b/4b7c48a4aeca81ec132e9e147dc19d103ded.pdf) | ||
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- Quality vs Quantity”- Improved Shot Prediction in Soccer using Strategic Features from Spatiotemporal Data-Paper, MIT Sloan Sports Analytics Conference'15 [[Paper]](https://s3-us-west-1.amazonaws.com/disneyresearch/wp-content/uploads/20150308192147/Quality-vs-Quantity%E2%80%9D-Improved-Shot-Prediction-in-Soccer-using-Strategic-Features-from-Spatiotemporal-Data-Paper.pdf) | ||
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### 2014 | ||
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- How to get an open shot: Analyzing team movement in basketball using tracking data, MIT SLOAN'14 [[Paper]](https://s3-us-west-1.amazonaws.com/disneyresearch/wp-content/uploads/20141125014436/How-to-Get-an-Open-Shot-Analyzing-Team-Movement-in-Basketball-using-Tracking-Data-Paper.pdf) | ||
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### 2013 | ||
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- Take your eyes off the ball: Improving ball-tracking by focusing on team play, CVIU'13 [[Paper]](https://infoscience.epfl.ch/record/185107/files/top_infosci_1.pdf) | ||
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- Detecting and tracking sports players with random forests and context-conditioned motion models, CVPR'13 [[Paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Liu_Tracking_Sports_Players_2013_CVPR_paper.pdf) | ||
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- Representing and discovering adversarial team behaviors using player roles, CVPR'13 [[Paper]](http://openaccess.thecvf.com/content_cvpr_2013/papers/Lucey_Representing_and_Discovering_2013_CVPR_paper.pdf) | ||
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- Recognising team activities from noisy data, CVPRW'13 [[Paper]](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W19/papers/Bialkowski_Recognising_Team_Activities_2013_CVPR_paper.pdf) | ||
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### 2012 | ||
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- Point-less calibration: Camera parameters from gradient-based alignment to edge images, WACV'12 [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6163012) | ||
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## Datasets Available | ||
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- Volleyball Dataset [[Link]](https://github.com/mostafa-saad/deep-activity-rec) | ||
- SoccerTrack Dataset [[Link]](https://www.kaggle.com/datasets/atomscott/soccertrack) | ||
- TeamTrack Dataset [[Link]](https://www.kaggle.com/datasets/atomscott/teamtrack) | ||
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## Credits | ||
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This curated list is a fork of the original [awesome-computer-vision-in-sports](https://github.com/avijit9/awesome-computer-vision-in-sports) repository created by [Avijit](https://github.com/avijit9). The purpose of this fork is to continue maintaining the list, including updates and new contributions. Most additions from before 2022 are from the original. | ||
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A big thank you to Avijit for creating the original repository and setting the foundation for this resource. All credit for the initial list goes to the original contributors and Avijit. Any new additions or updates post-fork are contributions from the community and this repository's maintainers. | ||
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## Licensing | ||
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- **Original List**: The content and structure of the original [awesome-computer-vision-in-sports](https://github.com/avijit9/awesome-computer-vision-in-sports) list adhere to its original licensing as specified by its creator, [Avijit](https://github.com/avijit9). | ||
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- **Updates and Additions**: Any updates, additions, or changes made post-fork adhere to the licensing of the SportsLabKit package. | ||
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It's essential for users and contributors to be aware of and respect the associated licenses when using or contributing to this list or the broader package. |