Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)

• URL: http://arxiv.org/abs/2209.00451v1
• Date: Wed, 31 Aug 2022 01:22:38 GMT
• Title: Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)
• Authors: Sandro Hauri and Slobodan Vucetic
• Abstract summary: We propose a novel deep learning approach for group activity recognition (GAR) in team sports called NETS. We used a large tracking data set from 632 NBA games to evaluate our approach. The results show that NETS is capable of learning group activities with high accuracy, and that self- and weak-supervised training in NETS have a positive impact on GAR accuracy.
• Score: 10.259254824702554
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Like many team sports, basketball involves two groups of players who engage in collaborative and adversarial activities to win a game. Players and teams are executing various complex strategies to gain an advantage over their opponents. Defining, identifying, and analyzing different types of activities is an important task in sports analytics, as it can lead to better strategies and decisions by the players and coaching staff. The objective of this paper is to automatically recognize basketball group activities from tracking data representing locations of players and the ball during a game. We propose a novel deep learning approach for group activity recognition (GAR) in team sports called NETS. To efficiently model the player relations in team sports, we combined a Transformer-based architecture with LSTM embedding, and a team-wise pooling layer to recognize the group activity. Training such a neural network generally requires a large amount of annotated data, which incurs high labeling cost. To address scarcity of manual labels, we generate weak-labels and pretrain the neural network on a self-supervised trajectory prediction task. We used a large tracking data set from 632 NBA games to evaluate our approach. The results show that NETS is capable of learning group activities with high accuracy, and that self- and weak-supervised training in NETS have a positive impact on GAR accuracy.

Related papers

• Deep learning for action spotting in association football videos [64.10841325879996]
  The SoccerNet initiative organizes yearly challenges, during which participants from all around the world compete to achieve state-of-the-art performances. This paper traces the history of action spotting in sports, from the creation of the task back in 2018, to the role it plays today in research and the sports industry.
  arXiv  Detail & Related papers  (2024-10-02T07:56:15Z)
• ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis [5.609957071296952]
  We present ShuttleSet, the largest publicly-available badminton singles dataset with annotated stroke-level records. It contains 104 sets, 3,685 rallies, and 36,492 strokes in 44 matches between 2018 and 2021 with 27 top-ranking men's singles and women's singles players. ShuttleSet is manually annotated with a computer-aided labeling tool to increase the labeling efficiency and effectiveness of selecting the shot type.
  arXiv  Detail & Related papers  (2023-06-08T05:41:42Z)
• Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial Sensors [47.33629411771497]
  We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors. The dataset was recorded for two teams from separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist.
  arXiv  Detail & Related papers  (2023-05-22T15:25:29Z)
• 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)
• 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)
• Individual and Collective Performance Deteriorate in a New Team: A Case Study of CS:GO Tournaments [11.86905804972623]
  This study aims to answer how changing a team affects individual and collective performance in eSports tournaments. We collected data from professional tournaments of a popular first-person shooter game. After a player switched to a new team, both the individual and the collective performance dropped initially, and then slowly recovered.
  arXiv  Detail & Related papers  (2022-05-19T16:54:49Z)
• 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)
• Contrastive Learning for Sports Video: Unsupervised Player Classification [2.3633885460047765]
  We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams. We show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time)
  arXiv  Detail & Related papers  (2021-04-15T20:24:02Z)
• My Team Will Go On: Differentiating High and Low Viability Teams through Team Interaction [17.729317295204368]
  We train a viability classification model over a dataset of 669 10-minute text conversations of online teams. We find that a lasso regression model achieves an accuracy of.74--.92 AUC ROC under different thresholds of classifying viability scores.
  arXiv  Detail & Related papers  (2020-10-14T21:33:36Z)
• Social Adaptive Module for Weakly-supervised Group Activity Recognition [143.68241396839062]
  This paper presents a new task named weakly-supervised group activity recognition (GAR) It differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data. This eases us to collect and annotate a large-scale NBA dataset and thus raise new challenges to GAR.
  arXiv  Detail & Related papers  (2020-07-18T16:40:55Z)

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.