1,078 research outputs found
Ball 3D Localization From A Single Calibrated Image
Ball 3D localization in team sports has various applications including
automatic offside detection in soccer, or shot release localization in
basketball. Today, this task is either resolved by using expensive multi-views
setups, or by restricting the analysis to ballistic trajectories. In this work,
we propose to address the task on a single image from a calibrated monocular
camera by estimating ball diameter in pixels and use the knowledge of real ball
diameter in meters. This approach is suitable for any game situation where the
ball is (even partly) visible. To achieve this, we use a small neural network
trained on image patches around candidates generated by a conventional ball
detector. Besides predicting ball diameter, our network outputs the confidence
of having a ball in the image patch. Validations on 3 basketball datasets
reveals that our model gives remarkable predictions on ball 3D localization. In
addition, through its confidence output, our model improves the detection rate
by filtering the candidates produced by the detector. The contributions of this
work are (i) the first model to address 3D ball localization on a single image,
(ii) an effective method for ball 3D annotation from single calibrated images,
(iii) a high quality 3D ball evaluation dataset annotated from a single
viewpoint. In addition, the code to reproduce this research is be made freely
available at https://github.com/gabriel-vanzandycke/deepsport.Comment: 9 pages, CVSports202
Estimation of control area in badminton doubles with pose information from top and back view drone videos
The application of visual tracking to the performance analysis of sports
players in dynamic competitions is vital for effective coaching. In doubles
matches, coordinated positioning is crucial for maintaining control of the
court and minimizing opponents' scoring opportunities. The analysis of such
teamwork plays a vital role in understanding the dynamics of the game. However,
previous studies have primarily focused on analyzing and assessing singles
players without considering occlusion in broadcast videos. These studies have
relied on discrete representations, which involve the analysis and
representation of specific actions (e.g., strokes) or events that occur during
the game while overlooking the meaningful spatial distribution. In this work,
we present the first annotated drone dataset from top and back views in
badminton doubles and propose a framework to estimate the control area
probability map, which can be used to evaluate teamwork performance. We present
an efficient framework of deep neural networks that enables the calculation of
full probability surfaces. This framework utilizes the embedding of a Gaussian
mixture map of players' positions and employs graph convolution on their poses.
In the experiment, we verify our approach by comparing various baselines and
discovering the correlations between the score and control area. Additionally,
we propose a practical application for assessing optimal positioning to provide
instructions during a game. Our approach offers both visual and quantitative
evaluations of players' movements, thereby providing valuable insights into
doubles teamwork. The dataset and related project code is available at
https://github.com/Ning-D/Drone_BD_ControlAreaComment: 15 pages, 10 figures, to appear in Multimedia Tools and Application
Recommended from our members
State of the Art of Sports Data Visualization
In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially-growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real-world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in-game actions and trajectories), and meta-data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high-level discussion of sports visualization research informed by our analysis—identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR’s website: https://sportsdataviz.github.io/
Recommended from our members
A Novel Multi-View Table Tennis Umpiring Framework
This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status.
While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies.
In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development
Basketball game analyzing based on computer vision
As tremendous improvement in computer vision technology, various industries start to apply computer vision to analyze huge multimedia content. Sports as one of the biggest resource invested industries also step up to utilize this technology to enhance their sports intelligent products.
The thesis is following this development to provide prototype implementations of computer vision algorithms in sports industry. Main objective is to develop initial algorithms to solve play-field detection and player tracking in basketball game video. Play-field detection is an important task in sports video content analysis, as it provides the foundation for further operations such as object detection, object tracking or semantic event highlight and summarization. On the other hand, player tracking highlight player movements in critical events in basketball game. It is also a challenging task to develop effective and efficient player tracking in basketball video, due to factors such as pose variation, illumination change, occlusion, and motion blur.
This thesis proposed reliable and efficient prototype algorithms to address play- field detection and single player tracking. SURF algorithm is utilized and modified to offer precise location of play-field and overlay trajectory data to improve viewer’s experience on sports product. And compressive tracking algorithm implemented for the aim of capture and track single player in important events to reveal player’s secret tactics. Prototype implementation to meet the current needs in basketball video content analyzing field
Multi-sensor human action recognition with particular application to tennis event-based indexing
The ability to automatically classify human actions and activities using vi- sual sensors or by analysing body worn sensor data has been an active re- search area for many years. Only recently with advancements in both fields and the ubiquitous nature of low cost sensors in our everyday lives has auto- matic human action recognition become a reality. While traditional sports coaching systems rely on manual indexing of events from a single modality, such as visual or inertial sensors, this thesis investigates the possibility of cap- turing and automatically indexing events from multimodal sensor streams. In this work, we detail a novel approach to infer human actions by fusing multimodal sensors to improve recognition accuracy. State of the art visual action recognition approaches are also investigated. Firstly we apply these action recognition detectors to basic human actions in a non-sporting con- text. We then perform action recognition to infer tennis events in a tennis court instrumented with cameras and inertial sensing infrastructure. The system proposed in this thesis can use either visual or inertial sensors to au- tomatically recognise the main tennis events during play. A complete event retrieval system is also presented to allow coaches to build advanced queries, which existing sports coaching solutions cannot facilitate, without an inordi- nate amount of manual indexing. The event retrieval interface is evaluated against a leading commercial sports coaching tool in terms of both usability and efficiency
From Image-based Motion Analysis to Free-Viewpoint Video
The problems of capturing real-world scenes with cameras and automatically analyzing the visible motion have traditionally been in the focus of computer vision research. The photo-realistic rendition of dynamic real-world scenes, on the other hand, is a problem that has been investigated in the field of computer graphics. In this thesis, we demonstrate that the joint solution to all three of these problems enables the creation of powerful new tools that are benecial for both research disciplines. Analysis and rendition of real-world scenes with human actors are amongst the most challenging problems. In this thesis we present new algorithmic recipes to attack them. The dissertation consists of three parts: In part I, we present novel solutions to two fundamental problems of human motion analysis. Firstly, we demonstrate a novel hybrid approach for markerfree human motion capture from multiple video streams. Thereafter, a new algorithm for automatic non-intrusive estimation of kinematic body models of arbitrary moving subjects from video is detailed. In part II of the thesis, we demonstrate that a marker-free motion capture approach makes possible the model-based reconstruction of free-viewpoint videos of human actors from only a handful of video streams. The estimated 3D videos enable the photo-realistic real-time rendition of a dynamic scene from arbitrary novel viewpoints. Texture information from video is not only applied to generate a realistic surface appearance, but also to improve the precision of the motion estimation scheme. The commitment to a generic body model also allows us to reconstruct a time-varying reflectance description of an actor`s body surface which allows us to realistically render the free-viewpoint videos under arbitrary lighting conditions. A novel method to capture high-speed large scale motion using regular still cameras and the principle of multi-exposure photography is described in part III. The fundamental principles underlying the methods in this thesis are not only applicable to humans but to a much larger class of subjects. It is demonstrated that, in conjunction, our proposed algorithmic recipes serve as building blocks for the next generation of immersive 3D visual media.Die Entwicklung neuer Algorithmen zur optischen Erfassung und Analyse der
Bewegung in dynamischen Szenen ist einer der Forschungsschwerpunkte in der
computergestützten Bildverarbeitung. Während im maschinellen Bildverstehen
das Augenmerk auf der Extraktion von Informationen liegt, konzentriert sich die
Computergrafik auf das inverse Problem, die fotorealistische Darstellung bewegter Szenen. In jüngster Vergangenheit haben sich die beiden Disziplinen kontinuierlich angenähert, da es eine Vielzahl an herausfordernden wissenschaftlichen Fragestellungen gibt, die eine gemeinsame Lösung des Bilderfassungs-, des Bildanalyse- und des Bildsyntheseproblems verlangen.
Zwei der schwierigsten Probleme, welche für Forscher aus beiden Disziplinen
eine große Relevanz besitzen, sind die Analyse und die Synthese von dynamischen
Szenen, in denen Menschen im Mittelpunkt stehen. Im Rahmen dieser
Dissertation werden Verfahren vorgestellt, welche die optische Erfassung dieser
Art von Szenen, die automatische Analyse der Bewegungen und die realistische
neue Darstellung im Computer erlauben. Es wid deutlich werden, dass eine Integration
von Algorithmen zur Lösung dieser drei Probleme in ein Gesamtsystem
die Erzeugung völlig neuartiger dreidimensionaler Darstellungen von Menschen
in Bewegung ermöglicht. Die Dissertation ist in drei Teile gegliedert:
Teil I beginnt mit der Beschreibung des Entwurfs und des Baus eines Studios
zur zeitsynchronen Erfassung mehrerer Videobildströme. Die im Studio aufgezeichneten
Multivideosequenzen dienen als Eingabedaten für die im Rahmen
dieser Dissertation entwickelten videogestützten Bewegunsanalyseverfahren und
die Algorithmen zur Erzeugung dreidimensionaler Videos.
Im Anschluß daran werden zwei neu entwickelte Verfahren vorgestellt,
die Antworten auf zwei fundamentale Fragen in der optischen Erfassung
menschlicher Bewegung geben, die Messung von Bewegungsparametern und
die Erzeugung von kinematischen Skelettmodellen. Das erste Verfahren ist ein
hybrider Algorithmus zur markierungslosen optischen Messung von Bewegunsgparametern
aus Multivideodaten. Der Verzicht auf optische Markierungen
wird dadurch ermöglicht, dass zur Bewegungsanalyse sowohl aus den Bilddaten
rekonstruierte Volumenmodelle als auch leicht zu erfassende Körpermerkmale
verwendet werden. Das zweite Verfahren dient der automatischen Rekonstruktion
eines kinematischen Skelettmodells anhand von Multivideodaten. Der Algorithmus
benötigt weder optischen Markierungen in der Szene noch a priori
Informationen über die Körperstruktur, und ist in gleicher Form auf Menschen,
Tiere und Objekte anwendbar.
Das Thema das zweiten Teils dieser Arbeit ist ein modellbasiertes Verfahrenzur Rekonstruktion dreidimensionaler Videos von Menschen in Bewegung aus
nur wenigen zeitsynchronen Videoströmen. Der Betrachter kann die errechneten
3D Videos auf einem Computer in Echtzeit abspielen und dabei interaktiv
einen beliebigen virtuellen Blickpunkt auf die Geschehnisse einnehmen. Im
Zentrum unseres Ansatzes steht ein silhouettenbasierter Analyse-durch-Synthese
Algorithmus, der es ermöglicht, ohne optische Markierungen sowohl die Form
als auch die Bewegung eines Menschen zu erfassen. Durch die Berechnung
zeitveränderlicher Oberächentexturen aus den Videodaten ist gewährleistet,
dass eine Person aus jedem beliebigen Blickwinkel ein fotorealistisches Erscheinungsbild
besitzt. In einer ersten algorithmischen Erweiterung wird gezeigt, dass
die Texturinformation auch zur Verbesserung der Genauigkeit der Bewegunsgssch
ätzung eingesetzt werden kann. Zudem ist es durch die Verwendung eines
generischen Körpermodells möglich, nicht nur dynamische Texturen sondern
sogar dynamische Reektionseigenschaften der Körperoberäche zu messen.
Unser Reektionsmodell besteht aus einer parametrischen BRDF für jeden Texel
und einer dynamischen Normalenkarte für die gesamte Körperoberäche. Auf
diese Weise können 3D Videos auch unter völlig neuen simulierten Beleuchtungsbedingungen
realistisch wiedergegeben werden.
Teil III dieser Arbeit beschreibt ein neuartiges Verfahren zur optischen
Messung sehr schneller Bewegungen. Bisher erforderten optische Aufnahmen
von Hochgeschwindigkeitsbewegungen sehr teure Spezialkameras mit hohen
Bildraten. Im Gegensatz dazu verwendet die hier beschriebene Methode einfache
Digitalfotokameras und das Prinzip der Multiblitzfotograe. Es wird gezeigt, dass
mit Hilfe dieses Verfahrens sowohl die sehr schnelle artikulierte Handbewegung
des Werfers als auch die Flugparameter des Balls während eines Baseballpitches
gemessen werden können. Die hochgenau erfaßten Parameter ermöglichen es, die
gemessene Bewegung in völlig neuer Weise im Computer zu visualisieren.
Obgleich die in dieser Dissertation vorgestellten Verfahren vornehmlich der
Analyse und Darstellung menschlicher Bewegungen dienen, sind die grundlegenden
Prinzipien auch auf viele anderen Szenen anwendbar. Jeder der beschriebenen
Algorithmen löst zwar in erster Linie ein bestimmtes Teilproblem, aber in Ihrer
Gesamtheit können die Verfahren als Bausteine verstanden werden, welche die
nächste Generation interaktiver dreidimensionaler Medien ermöglichen werden
- …