mlsanityData to 7 Oct 2026

A Survey of Advanced Computer Vision Techniques for Sports

arXiv 2301.07583 ↗First posted 18 Jan 2023cs.CV cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Computer Vision developments are enabling significant advances in many fields, including sports. Many applications built on top of Computer Vision technologies, such as tracking data, are nowadays essential for every top-level analyst, coach, and even player. In this paper, we survey Computer Vision techniques that can help many sports-related studies gather vast amounts of data, such as Object Detection and Pose Estimation. We provide a use case for such data: building a model for shot speed estimation with pose data obtained using only Computer Vision models. Our model achieves a correlation of 67%. The possibility of estimating shot speeds enables much deeper studies about enabling the creation of new metrics and recommendation systems that will help athletes improve their performance, in any sport. The proposed methodology is easily replicable for many technical movements and is only limited by the availability of video data.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 54 of its 80 references that are in the map. First posted on arXiv 18 Jan 2023.

Builds on

54 of 80 references.

References · 54

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  7. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  8. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  10. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  11. 2015Fast R-CNNcited by 2,930
  12. 2018YOLOv3: An Incremental Improvementcited by 2,652
38 more
  1. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  2. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  3. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  4. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  5. 2019Objects as Pointscited by 1,172
  6. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
  7. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  8. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,061
  9. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  10. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
  11. 2016Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Imagecited by 692
  12. 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 615
  13. 2018DensePose: Dense Human Pose Estimation In The Wildcited by 573
  14. 2019Deep Learning for Image Super-resolution: A Surveycited by 368
  15. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  16. 2021PARE: Part Attention Regressor for 3D Human Body Estimationcited by 334
  17. 2016Convolutional Pose Machinescited by 325
  18. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 315
  19. 2021Focal Self-attention for Local-Global Interactions in Vision Transformerscited by 286
  20. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  21. 2020Monocular, One-stage, Regression of Multiple 3D Peoplecited by 216
  22. 2021Dynamic Head: Unifying Object Detection Heads with Attentionscited by 185
  23. 2019Learnable Triangulation of Human Posecited by 160
  24. 2017Towards Accurate Multi-person Pose Estimation in the Wildcited by 131
  25. 2014Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimationcited by 129
  26. 2017Single-Shot Refinement Neural Network for Object Detectioncited by 129
  27. 2015Human Pose Estimation with Iterative Error Feedbackcited by 121
  28. 2015DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimationcited by 117
  29. 2020Epipolar Transformerscited by 117
  30. 2016DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Modelcited by 96
  31. 2021Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimationcited by 94
  32. 2021You Only Learn One Representation: Unified Network for Multiple Taskscited by 86
  33. 2021CBNet: A Composite Backbone Network Architecture for Object Detectioncited by 77
  34. 2015Flowing ConvNets for Human Pose Estimation in Videoscited by 55
  35. 2021Conditional Directed Graph Convolution for 3D Human Pose Estimationcited by 52
  36. 2017Detect-and-Track: Efficient Pose Estimation in Videoscited by 51
  37. 2020PP-YOLO: An Effective and Efficient Implementation of Object Detectorcited by 44
  38. 2021PP-YOLOv2: A Practical Object Detectorcited by 30

50 most cited of 54 All 54 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025SoccerNet-v3D: Leveraging Sports Broadcast Replays for 3D Scene Understandingcited by 4
  2. 2026Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Videocited by 0