Player Tracking and Identification in Ice Hockey

• URL: http://arxiv.org/abs/2110.03090v1
• Date: Wed, 6 Oct 2021 22:37:08 GMT
• Title: Player Tracking and Identification in Ice Hockey
• Authors: Kanav Vats, Pascale Walters, Mehrnaz Fani, David A. Clausi, John Zelek
• Abstract summary: This paper introduces an automated system to track and identify players in broadcast NHL hockey videos. The system is composed of three components (1) Player tracking, (2) Team identification and (3) Player identification. For team identification, the away-team jerseys are grouped into a single class and home-team jerseys are grouped in classes according to their jersey color. A novel player identification model is introduced that utilizes a temporal one-dimensional convolutional network to identify players from player bounding box sequences.
• Score: 9.577770317771087
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Tracking and identifying players is a fundamental step in computer vision-based ice hockey analytics. The data generated by tracking is used in many other downstream tasks, such as game event detection and game strategy analysis. Player tracking and identification is a challenging problem since the motion of players in hockey is fast-paced and non-linear when compared to pedestrians. There is also significant camera panning and zooming in hockey broadcast video. Identifying players in ice hockey is challenging since the players of the same team look almost identical, with the jersey number the only discriminating factor between players. In this paper, an automated system to track and identify players in broadcast NHL hockey videos is introduced. The system is composed of three components (1) Player tracking, (2) Team identification and (3) Player identification. Due to the absence of publicly available datasets, the datasets used to train the three components are annotated manually. Player tracking is performed with the help of a state of the art tracking algorithm obtaining a Multi-Object Tracking Accuracy (MOTA) score of 94.5%. For team identification, the away-team jerseys are grouped into a single class and home-team jerseys are grouped in classes according to their jersey color. A convolutional neural network is then trained on the team identification dataset. The team identification network gets an accuracy of 97% on the test set. A novel player identification model is introduced that utilizes a temporal one-dimensional convolutional network to identify players from player bounding box sequences. The player identification model further takes advantage of the available NHL game roster data to obtain a player identification accuracy of 83%.

Related papers

• SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap [102.5232204867158]
  We formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration. Our experiments show that GSR is a challenging novel task, which opens the field for future research.
  arXiv  Detail & Related papers  (2024-04-17T12:53:45Z)
• SoccerNet 2023 Challenges Results [165.5977813812761]
  SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes.
  arXiv  Detail & Related papers  (2023-09-12T07:03:30Z)
• A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification [75.93186954061943]
  Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification task, our method obtains an overall performance of 57.83% average-mAP by combining it with other modalities.
  arXiv  Detail & Related papers  (2022-11-22T15:23:53Z)
• A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications [60.3327085463545]
  We present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, diving and badminton. We develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.
  arXiv  Detail & Related papers  (2022-06-02T13:19:36Z)
• Evaluating deep tracking models for player tracking in broadcast ice hockey video [20.850267622473176]
  Player tracking is a challenging problem since the motion of players in hockey is fast-paced and non-linear. We compare and contrast several state-of-the-art tracking algorithms and analyze their performance and failure modes in ice hockey.
  arXiv  Detail & Related papers  (2022-05-22T22:56:31Z)
• Automated player identification and indexing using two-stage deep learning network [0.23610495849936355]
  We propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos.
  arXiv  Detail & Related papers  (2022-04-26T02:59:03Z)
• 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)
• Efficient tracking of team sport players with few game-specific annotations [1.052782170493037]
  We propose a new generic method to track team sport players during a full game thanks to few human annotations collected via a semi-interactive system. Non-ambiguous tracklets and their appearance features are automatically generated with a detection and a reidentification network both pre-trained on public datasets. We demonstrate the efficiency of our approach on a challenging rugby sevens dataset.
  arXiv  Detail & Related papers  (2022-04-08T13:11:30Z)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• Ice hockey player identification via transformers [11.28395713457468]
  We introduce a transformer network for recognizing players through their jersey numbers in broadcast National Hockey League (NHL) videos. The proposed network performs better than the previous benchmark on the dataset used. We also utilize player shifts available in the NHL play-by-play data by reading the game time using optical character recognition (OCR) to get the players on the ice rink at a certain game time.
  arXiv  Detail & Related papers  (2021-11-22T21:10:26Z)
• Player Identification in Hockey Broadcast Videos [18.616544581429835]
  We present a deep convolutional neural network approach to solve the problem of hockey player identification in NHL broadcast. We employ a secondary 1-dimensional convolutional neural network as a late score-level fusion method to classify the output of the ResNet+LSTM network. This achieves an overall player identification accuracy score over 87% on the test split of our new dataset.
  arXiv  Detail & Related papers  (2020-09-05T01:30:15Z)

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.