Foul prediction with estimated poses from soccer broadcast video

• URL: http://arxiv.org/abs/2402.09650v1
• Date: Thu, 15 Feb 2024 01:25:19 GMT
• Title: Foul prediction with estimated poses from soccer broadcast video
• Authors: Jiale Fang, Calvin Yeung, Keisuke Fujii
• Abstract summary: We introduce an innovative deep learning approach for anticipating soccer fouls. This method integrates video data, bounding box positions, image details, and pose information by curating a novel soccer foul dataset. Our results have important implications for a deeper understanding of foul play in soccer.
• Score: 0.9002260638342727
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in computer vision have made significant progress in tracking and pose estimation of sports players. However, there have been fewer studies on behavior prediction with pose estimation in sports, in particular, the prediction of soccer fouls is challenging because of the smaller image size of each player and of difficulty in the usage of e.g., the ball and pose information. In our research, we introduce an innovative deep learning approach for anticipating soccer fouls. This method integrates video data, bounding box positions, image details, and pose information by curating a novel soccer foul dataset. Our model utilizes a combination of convolutional and recurrent neural networks (CNNs and RNNs) to effectively merge information from these four modalities. The experimental results show that our full model outperformed the ablated models, and all of the RNN modules, bounding box position and image, and estimated pose were useful for the foul prediction. Our findings have important implications for a deeper understanding of foul play in soccer and provide a valuable reference for future research and practice in this area.

Related papers

• Towards Active Learning for Action Spotting in Association Football Videos [59.84375958757395]
  Analyzing football videos is challenging and requires identifying subtle and diverse-temporal patterns. Current algorithms face significant challenges when learning from limited annotated data. We propose an active learning framework that selects the most informative video samples to be annotated next.
  arXiv  Detail & Related papers  (2023-04-09T11:50:41Z)
• Event Detection in Football using Graph Convolutional Networks [0.0]
  We show how to model the players and the ball in each frame of the video sequence as a graph. We present the results for graph convolutional layers and pooling methods that can be used to model the temporal context present around each action.
  arXiv  Detail & Related papers  (2023-01-24T14:52:54Z)
• Graph Neural Networks to Predict Sports Outcomes [0.0]
  We introduce a sport-agnostic graph-based representation of game states. We then use our proposed graph representation as input to graph neural networks to predict sports outcomes.
  arXiv  Detail & Related papers  (2022-07-28T14:45:02Z)
• SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos [62.686484228479095]
  We propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each. The dataset is fully annotated with bounding boxes and tracklet IDs. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved.
  arXiv  Detail & Related papers  (2022-04-14T12:22:12Z)
• Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking [98.91894395941766]
  We propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame. Specifically, we derive this prediction of dynamics through a graph neural network(GNN) that explicitly accounts for both spatial-temporal and visual information. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks.
  arXiv  Detail & Related papers  (2021-06-07T16:36:50Z)
• "Why Would I Trust Your Numbers?" On the Explainability of Expected Values in Soccer [5.825190876052149]
  We introduce an explainable Generalized Additive Model that estimates the expected value for shots. We represent the locations of shots by fuzzily assigning the shots to designated zones on the pitch that practitioners are familiar with. Our experimental evaluation shows that our model is as accurate as existing models, while being easier to explain to soccer practitioners.
  arXiv  Detail & Related papers  (2021-05-27T10:05:00Z)
• Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting [61.92132798351982]
  We distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset. We leverage it to provide 3 ways of representing the calibration results along with player localization. We exploit those representations within the current best architecture for the action spotting task of SoccerNet-v2.
  arXiv  Detail & Related papers  (2021-04-19T14:21:05Z)
• Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning [0.0]
  We will introduce a two-part solution: an open-source Player Tracking model and a new approach to evaluate these players based solely on Deep Reinforcement Learning. Our tracking model was trained in a supervised fashion on datasets we will also release, and our Evaluation Model relies only on simulations of virtual soccer games. We term our new approach Expected Discounted Goal (EDG) as it represents the number of goals a team can score or concede from a particular state.
  arXiv  Detail & Related papers  (2021-01-13T23:26:17Z)
• SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos [71.72665910128975]
  SoccerNet-v2 is a novel large-scale corpus of manual annotations for the SoccerNet video dataset. We release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos. We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection.
  arXiv  Detail & Related papers  (2020-11-26T16:10:16Z)
• Self-Supervised Small Soccer Player Detection and Tracking [8.851964372308801]
  State-of-the-art tracking algorithms achieve impressive results in scenarios on which they have been trained for, but they fail in challenging ones such as soccer games. This is frequently due to the player small relative size and the similar appearance among players of the same team. We propose a self-supervised pipeline which is able to detect and track low-resolution soccer players under different recording conditions without any need of ground-truth data.
  arXiv  Detail & Related papers  (2020-11-20T10:57:18Z)
• Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification [53.735029033681435]
  Transfer learning is a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. In this work, we demonstrate that adversarially-trained models transfer better than non-adversarially-trained models.
  arXiv  Detail & Related papers  (2020-07-11T22:48:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.