MuLMINet: Multi-Layer Multi-Input Transformer Network with Weighted Loss
- URL: http://arxiv.org/abs/2307.08262v2
- Date: Sun, 7 Apr 2024 15:47:41 GMT
- Title: MuLMINet: Multi-Layer Multi-Input Transformer Network with Weighted Loss
- Authors: Minwoo Seong, Jeongseok Oh, SeungJun Kim,
- Abstract summary: We present a Multi-Layer Multi-Input Transformer Network (MuLMINet) that leverages professional badminton player match data to accurately predict future shot types and area coordinates.
Our approach resulted in achieving the runner-up (2nd place) in the IJCAI CoachAI Badminton Challenge 2023, Track 2.
- Score: 6.854732863866882
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The increasing use of artificial intelligence (AI) technology in turn-based sports, such as badminton, has sparked significant interest in evaluating strategies through the analysis of match video data. Predicting future shots based on past ones plays a vital role in coaching and strategic planning. In this study, we present a Multi-Layer Multi-Input Transformer Network (MuLMINet) that leverages professional badminton player match data to accurately predict future shot types and area coordinates. Our approach resulted in achieving the runner-up (2nd place) in the IJCAI CoachAI Badminton Challenge 2023, Track 2. To facilitate further research, we have made our code publicly accessible online, contributing to the broader research community's knowledge and advancements in the field of AI-assisted sports analysis.
Related papers
- Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies [46.1232919707345]
We present the tennis match simulation environment textitMatch Point AI, in which different agents can compete against real-world data-driven bot strategies.
First experiments show that generated shot-by-shot data of simulated tennis matches show realistic characteristics when compared to real-world data.
At the same time, reasonable shot placement strategies emerge, which share similarities to the ones found in real-world tennis matches.
arXiv Detail & Related papers (2024-08-12T07:22:46Z) - Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset [16.407837909069073]
We introduce the VideoBadminton dataset derived from high-quality badminton footage.
The introduction of VideoBadminton could not only serve for badminton action recognition but also provide a dataset for recognizing fine-grained actions.
arXiv Detail & Related papers (2024-03-19T02:52:06Z) - Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset [13.502952342104644]
We provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022.
To benchmark existing work with ShuttleSet22, we hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge @ IJCAI 2023.
arXiv Detail & Related papers (2023-06-27T17:57:34Z) - 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) - ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles
for Stroke Forecasting in Badminton [18.524164548051417]
This paper focuses on objectively judging what and where to return strokes in turn-based sports.
We propose a novel Position-aware Fusion of Rally Progress and Player Styles framework (ShuttleNet) that incorporates rally progress and information of the players.
arXiv Detail & Related papers (2021-12-02T08:14:23Z) - AI-enabled Prediction of eSports Player Performance Using the Data from
Heterogeneous Sensors [12.071865017583502]
We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors.
The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network.
The proposed solution has a number of promising applications for Pro eSports teams as well as a learning tool for amateur players.
arXiv Detail & Related papers (2020-12-07T07:31:53Z) - Game Plan: What AI can do for Football, and What Football can do for AI [83.79507996785838]
Predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision.
We illustrate that football analytics is a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI.
arXiv Detail & Related papers (2020-11-18T10:26:02Z) - Learning from Learners: Adapting Reinforcement Learning Agents to be
Competitive in a Card Game [71.24825724518847]
We present a study on how popular reinforcement learning algorithms can be adapted to learn and to play a real-world implementation of a competitive multiplayer card game.
We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style.
arXiv Detail & Related papers (2020-04-08T14:11:05Z) - Neural MMO v1.3: A Massively Multiagent Game Environment for Training
and Evaluating Neural Networks [48.5733173329785]
We present Neural MMO, a massively multiagent game environment inspired by MMOs.
We discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO.
arXiv Detail & Related papers (2020-01-31T18:50:02Z)
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.