Space evaluation based on pitch control using drone video in Ultimate
- URL: http://arxiv.org/abs/2409.14588v1
- Date: Tue, 3 Sep 2024 01:19:02 GMT
- Title: Space evaluation based on pitch control using drone video in Ultimate
- Abstract summary: This study focuses on the 3-on-3 format, which is widely practiced in Ultimate, and evaluates space during offensive play.
The model is derived from the pitch control model of soccer and adapted to the rules of Ultimate, where the player holding the disc is stationary.
The integration of position and distance weights with pitch control values enables the derivation of space evaluation metrics.
- Score: 8.896958616329611
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Ultimate is a sport in which teams of seven players compete for points by passing a disc into the end zone. A distinctive aspect of Ultimate is that the player holding the disc is unable to move, underscoring the significance of creating space to receive passes. Despite extensive research into space evaluation in sports such as football and basketball, there is a paucity of information available for Ultimate. This study focuses on the 3-on-3 format, which is widely practiced in Ultimate, and evaluates space during offensive play. The data collection process entailed the use of drones for filming and the subsequent correction of the angles for the purpose of obtaining positional data. The model is derived from the pitch control model of soccer and adapted to the rules of Ultimate, where the player holding the disc is stationary. The integration of position and distance weights with pitch control values enables the derivation of space evaluation metrics. The findings of this study indicate that movement to create space and accurate passing into that space are both significant factors in scoring. The code is available at https://github.com/shunsuke-iwashita/USO.
Related papers
- SoccerLens: Grounded Soccer Video Understanding Beyond Accuracy [52.09081885158698]
Vision-language models (VLMs) have recently shown strong potential in soccer video understanding.<n>Existing evaluation protocols focus on classification accuracy and do not assess visual grounding.<n>We introduce SoccerLens, a benchmark for grounded soccer video understanding.
arXiv Detail & Related papers (2026-05-10T15:18:08Z) - Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data [0.0]
Traditional approaches to measuring visual exploratory behavior in soccer rely on counting visual exploratory actions (VEAs) based on rapid movements exceeding 125/s.<n>This research introduces a formulaic continuous vision layer to quantify players' visual perception from pose-enhanced tracking.<n>We demonstrate that aggregated visual metrics are predictive of controlled pitch value gained at the end of dribbling actions using 32 games of synchronized pose-enhanced tracking data and on-ball event data from the 2024 Copa America.
arXiv Detail & Related papers (2026-02-19T20:17:23Z) - Right Move, Right Time: Multi-Sport Space Evaluation Platform for Ultimate Frisbee, Basketball, and Soccer [3.4384744577889297]
We present an open, sport-agnostic platform that turns tracking into comparable spatial measures across professional Ultimate, basketball, and soccer.<n>Our workflow standardizes inputs, provides timing-aware spatial evaluations, and makes it possible to reuse the same analysis across sports.
arXiv Detail & Related papers (2026-01-26T21:18:53Z) - SoccerNet 2025 Challenges Results [205.71032061537747]
SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open effort, dedicated to advancing computer vision research in football video understanding.<n>This year's challenges span four vision-based tasks: Team Ball Action Spotting, Monocular Depth Estimation, Multi-View Foul Recognition, and Game State Reconstruction.<n>Report presents the results of each challenge, highlights the top-performing solutions, and provides insights into the progress made by the community.
arXiv Detail & Related papers (2025-08-26T16:37:07Z) - Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals [9.570401141299756]
In Ultimate Frisbee, points are scored by passing a disc and catching it in the opposing team's end zone.<n>Current literature has ignored quantitative evaluations of when players initiate such unlabeled movements in game situations.<n>We propose a quantitative evaluation method for movement initiation timing in Ultimate Frisbee.
arXiv Detail & Related papers (2025-08-25T02:42:08Z) - What Makes a Dribble Successful? Insights From 3D Pose Tracking Data [15.488712139146381]
This study explores how pose tracking data can improve our understanding of dribbling skills.<n>We extract novel pose-based features from 1,736 dribbles in the 2022/23 Champions League season.<n>Our results indicate that features capturing the attacker's balance and the alignment of the orientation between the attacker and defender are informative for predicting dribble success.
arXiv Detail & Related papers (2025-06-25T15:01:30Z) - Space evaluation at the starting point of soccer transitions [0.8009842832476994]
We propose OBPV (Off-Ball Positioning Value) to evaluate space across the pitch, including the starting points of transitions.<n>Experiments using La Liga 2023/24 season tracking and event data show that OBPV highlights effective space utilization during counter-attacks.
arXiv Detail & Related papers (2025-05-17T06:28:06Z) - TT3D: Table Tennis 3D Reconstruction [11.84899291358663]
We propose a novel approach for reconstructing precise 3D ball trajectories from online table tennis match recordings.
Our method leverages the underlying physics of the ball's motion to identify the bounce state that minimizes the reprojection error of the ball's flying trajectory.
A key advantage of our approach is its ability to infer ball spin without relying on human pose estimation or racket tracking.
arXiv Detail & Related papers (2025-04-14T09:37:47Z) - WorldPose: A World Cup Dataset for Global 3D Human Pose Estimation [67.28831601491447]
WorldPose is a novel dataset for advancing research in multi-person global pose estimation in the wild.
We exploit the static multi-view setup of HD cameras to recover the 3D player poses and motions with unprecedented accuracy.
The resulting dataset comprises more than 80 sequences with approx 2.5 million 3D poses and a total traveling distance of over 120 km.
arXiv Detail & Related papers (2025-01-06T05:24:38Z) - 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) - Passing Heatmap Prediction Based on Transformer Model and Tracking Data [0.0]
This research presents a novel deep-learning network architecture which is capable to predict the potential end location of passes.
Once analysed more than 28,000 pass events, a robust prediction can be achieved with more than 0.7 Top-1 accuracy.
And based on the prediction, a better understanding of the pitch control and pass option could be reached to measure players' off-ball movement contribution to defensive performance.
arXiv Detail & Related papers (2023-09-04T11:14:22Z) - Estimation of control area in badminton doubles with pose information
from top and back view drone videos [11.679451300997016]
We present the first annotated drone dataset from top and back views in badminton doubles.
We propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance.
arXiv Detail & Related papers (2023-05-07T11:18:39Z) - Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial
Sports Field Registration [0.0]
We combine advances in 2D human pose estimation and camera calibration via partial sports field registration to demonstrate an avenue for collecting valid large-scale kinematic datasets.
We generate a synthetic dataset of more than 10k images in Unreal Engine 5 with different viewpoints, running styles, and body types.
arXiv Detail & Related papers (2023-04-10T07:41:44Z) - An Effective Motion-Centric Paradigm for 3D Single Object Tracking in
Point Clouds [50.19288542498838]
3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving.
Current approaches all follow the Siamese paradigm based on appearance matching.
We introduce a motion-centric paradigm to handle LiDAR SOT from a new perspective.
arXiv Detail & Related papers (2023-03-21T17:28:44Z) - KaliCalib: A Framework for Basketball Court Registration [1.052782170493037]
This paper describes a new basketball court registration framework in the context of the MMSports 2022 camera calibration challenge.
The method is based on the estimation by an encoder-decoder network of the positions of keypoints sampled with perspective-aware constraints.
arXiv Detail & Related papers (2022-09-16T08:52:29Z) - 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) - Gravity-Aware Monocular 3D Human-Object Reconstruction [73.25185274561139]
This paper proposes a new approach for joint markerless 3D human motion capture and object trajectory estimation from monocular RGB videos.
We focus on scenes with objects partially observed during a free flight.
In the experiments, our approach achieves state-of-the-art accuracy in 3D human motion capture on various metrics.
arXiv Detail & Related papers (2021-08-19T17:59:57Z) - Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
This paper attempts to address the problem of drones detection from other flying drones variations.
The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging.
To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
arXiv Detail & Related papers (2021-03-31T17:43:31Z) - Learning to Play Cup-and-Ball with Noisy Camera Observations [2.6931502677545947]
We present a learning model based control strategy for the cup-and-ball game.
A Universal Robots UR5e manipulator arm learns to catch a ball in one of the cups on a Kendama.
arXiv Detail & Related papers (2020-07-19T02:22:36Z) - Fusing Motion Patterns and Key Visual Information for Semantic Event
Recognition in Basketball Videos [87.29451470527353]
We propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos.
An algorithm is proposed to estimate the global motions from the mixed motions based on the intrinsic property of camera adjustments.
A two-stream 3D CNN framework is utilized for group activity recognition over the separated global and local motion patterns.
arXiv Detail & Related papers (2020-07-13T10:15:44Z)
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.