A Tracking System For Baseball Game Reconstruction
- URL: http://arxiv.org/abs/2003.03856v1
- Date: Sun, 8 Mar 2020 22:04:54 GMT
- Title: A Tracking System For Baseball Game Reconstruction
- Authors: Nina Wiedemann, Carlos Dietrich, Claudio T. Silva
- Abstract summary: We propose a system that captures the movements of the pitcher, the batter, and the ball in a high level of detail.
We demonstrate on a large database of videos that our methods achieve comparable results as previous systems.
In addition, state-of-the-art AI techniques are incorporated to augment the amount of information that is made available for players, coaches, teams, and fans.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The baseball game is often seen as many contests that are performed between
individuals. The duel between the pitcher and the batter, for example, is
considered the engine that drives the sport. The pitchers use a variety of
strategies to gain competitive advantage against the batter, who does his best
to figure out the ball trajectory and react in time for a hit. In this work, we
propose a system that captures the movements of the pitcher, the batter, and
the ball in a high level of detail, and discuss several ways how this
information may be processed to compute interesting statistics. We demonstrate
on a large database of videos that our methods achieve comparable results as
previous systems, while operating solely on video material. In addition,
state-of-the-art AI techniques are incorporated to augment the amount of
information that is made available for players, coaches, teams, and fans.
Related papers
- SkillMimic: Learning Reusable Basketball Skills from Demonstrations [85.23012579911378]
We propose SkillMimic, a data-driven approach that mimics both human and ball motions to learn a wide variety of basketball skills.
SkillMimic employs a unified configuration to learn diverse skills from human-ball motion datasets.
The skills acquired by SkillMimic can be easily reused by a high-level controller to accomplish complex basketball tasks.
arXiv Detail & Related papers (2024-08-12T15:19:04Z) - PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics [13.928557561312026]
PitcherNet is an end-to-end automated system that analyzes pitcher kinematics directly from live broadcast video.
It achieves robust analysis results with 96.82% accuracy in pitcher tracklet identification.
PitcherNet paves the way for the future of baseball analytics by optimizing pitching strategies, preventing injuries, and unlocking a deeper understanding of pitcher mechanics.
arXiv Detail & Related papers (2024-05-13T01:03:06Z) - Understanding why shooters shoot -- An AI-powered engine for basketball
performance profiling [70.54015529131325]
Basketball is dictated by many variables, such as playstyle and game dynamics.
It is crucial that the performance profiles can reflect the diverse playstyles.
We present a tool that can visualize player performance profiles in a timely manner.
arXiv Detail & Related papers (2023-03-17T01:13:18Z) - 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) - Performance Prediction in Major League Baseball by Long Short-Term
Memory Networks [0.35092739016434554]
We use the sequential model Long Short-Term Memory as our main method to solve the home run prediction problem in Major League Baseball.
Our results show that Long Short-Term Memory has better performance than others and has the ability to make more exact predictions.
arXiv Detail & Related papers (2022-06-20T09:01:44Z) - Estimating the Effect of Team Hitting Strategies Using Counterfactual
Virtual Simulation in Baseball [8.640691759862918]
In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies.
We propose a new method for estimating the effect using counterfactual batting simulation.
arXiv Detail & Related papers (2022-06-04T01:33:04Z) - 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) - 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) - Computing an Optimal Pitching Strategy in a Baseball At-Bat [19.933511825856126]
A baseball at-bat is a matchup between a pitcher and a batter.
We propose a novel model of this encounter as a zero-sum game.
In principle, this game can be solved using classical approaches.
arXiv Detail & Related papers (2021-10-08T18:09:08Z) - Learning To Describe Player Form in The MLB [5.612162576040905]
We present a novel, contrastive learning-based framework for describing player form in the MLB.
Our form representations contain information about how players impact the course of play, not present in traditional, publicly available statistics.
These embeddings could be utilized to predict both in-game and game-level events, such as the result of an at-bat or the winner of a game.
arXiv Detail & Related papers (2021-09-11T13:42:07Z) - 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)
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.