Multi-camera analysis of soccer sequences

The automatic detection of meaningful phases in a soccer game depends on the accurate localization of players and the ball at each moment. However, the automatic analysis of soccer sequences is a challenging task due to the presence of fast moving multiple objects. For this purpose, we present a multi-camera analysis system that yields the position of the ball and players on a common ground plane. The detection in each camera is based on a code-book algorithm and different features are used to classify the detected blobs. The detection results of each camera are transformed using homography to a virtual top-view of the playing field. Within this virtual top-view we merge trajectory information of the different cameras allowing to refine the found positions. In this paper, we evaluate the system on a public SOCCER dataset and end with a discussion of possible improvements of the dataset

Ball-path inference based on a combination of audio and video clues in tennis video sequences

Tennis-sports analysis is attracting much attention in content-analysis research and professional applications.This paper presents a scheme for sports analysis employing an automatic tennis ball-path inference driven by a combination of auditory and visual information. The ball-path inference is implemented for tactics analysis.Since ball tracking remains to be a challenging issue in practice, we use a non-tracking approach for ball-path inference. We propose an effective serving-player detection for achieving an accurate match between a sequence of racket-hit moments and the position of the hitting player in the corresponding video frames. Experimental results have shown that the proposed system can reliably detect the serving-player and classify into different categories, such as left-court/right-court service and frontcourt/ back-court service. Therefore, our system can be utilized for an effective and automatic extraction of various tennis events, performance and tactics analysis with high reliability

Narrative Bytes : Data-Driven Content Production in Esports

Esports - video games played competitively that are broadcast to large audiences - are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a large-scale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers

DETEKSI BOLA PADA KONVERSI POIN PERMAINAN TENIS MEJA BERBASIS VIDEO PROCESSING

Pendeteksian pergerakan bola pada tenis meja dengan metode background substraction and estimation adalah satu dari sekian banyak metode yang dipakai. Penentuan kondisi bola dengan Kalman filter, yaitu proses deteksi dan koreksi yang digunakan sangat cocok pada metode ini. Deteksi dan tracking bola dengan background substraction and estimation based on Kalman filter, membuat penelitian tugas akhir ini memiliki keunikan dari metode yang lain. Pada tugas akhir ini dirancang suatu program pengolahan video untuk mendeteksi pergerakan dan menentukan kondisi bola dari permainan tenis meja. Metode background subtraction and estimation digunakan sebagai pemisah antara background dan foreground, sehingga didapatkan objek yang akan di deteksi. Pendeteksian dan tracking bola dengan Kalman filter, sehingga sistem dapat menentukan bola saat melewati net dan garis. Pada deteksi bola berwarna oranye, saat jarak 1 meter dengan luas area 250 pixels menghasilkan akurasi sebesar 100%. Deteksi bola melewati garis, terdapat pada luas area 500 pixels dan 250 pixels dengan akurasi 100%. Sedangkan deteksi bola melewati net, terdapat pada luas area 500 pixels dengan akurasi 81,81%. Kata kunci : pengolahan video, deteksi gerakan, tenis meja, background subtraction, Kalman filter

Data-driven evaluation of on-field player performance in football using sensor and video technologies

Data has become increasingly relevant and used in football over the years. Technological development has made it possible to gather data from various aspects of the game. However, despite the growing popularity of sports analytics, relatively little research, especially qualitative, has been done on the topic. The purpose of this thesis is to create understanding and practices for taking advance of data for evaluation of on-field player performance in football using sensor and video technologies. This is done by identifying and combining technological possibilities with sports knowledge and suggesting an approach for data-driven evaluation of the on-field player performance. Review of previous literature and semi-structured theme interviews have been used as a method to achieve the purpose of the thesis. The findings of the thesis show that data can be used in the evaluation of on-field player performance in football by assessing players’ physical, technical, tactical, and mental attributes. These attributes have several different metrics, the value of which depends on several factors such as the team's objectives. Furthermore, an approach is presented in the thesis which suggests that the selection of team-specific attributes and metrics guides the user to consider which data is needed to be able to evaluate the desired metrics, which then can be linked to certain technologies and analytical solutions presented in the thesis

Análisis de actividad en vídeos de baloncesto

El gran abanico de posibilidades de trabajo que ofrecen los sistemas de análisis de contenido en vídeos deportivos hizo que se desarrollase en el grupo de procesamiento de vídeo (VPU-Lab) de la Escuela Politécnica Superior de la Universidad Autónoma de Madrid una aplicación interactiva de detección y seguimiento de jugadores. Este prototipo consistía en la mejora de usabilidad e interacción con el usuario final de un sistema ya realizado previamente en el cual se conseguían buenos resultados tanto de detección como de seguimiento. Dicha aplicación se había creado tanto para deportes individuales como colectivos, pero en este último caso sólo trabajaba con vídeos de fútbol. Debido a esto, surgió una nueva línea de investigación que consistía en la adaptación de este prototipo a otro deporte colectivo como es el baloncesto. Por tanto, en este proyecto se ha trabajado sobre esa línea, solucionando la problemática presente y creando un primer prototipo sobre el cual seguir investigando y desarrollando. Se trabajó en la adaptación de la programación del sistema para que se pudiese ejecutar con un número distinto de vídeos entrantes. A continuación se modificó la interfaz gráfica para que pudiese representar de una forma óptima los nuevos resultados obtenidos, manteniendo toda la funcionalidad que tenía en el anterior prototipo. Seguidamente, se investigó la mejora de los resultados obtenidos en estos nuevos vídeos con el sistema que estaba implantando. Se trató de obtener los mejores resultados posibles tanto de detección como de seguimiento ajustando diferentes parámetros. Por último, puesto que la interfaz es la plataforma visible de este proyecto, se ha trabajado en mejorarla dotándola de un nuevo módulo de creación de la homografía que hace que se simplifique aún más el uso de esta interfaz por el usuario.The large range of functional possibilities that content analysis systems o er in sports videos has prompted the development, in Escuela Politécnica Superior of the Universidad Autonoma de Madrid, of an interactive detection and tracking application for players. This prototype consisted of a usability and end-user interaction improvement of a previously known system in which both detection and tracking results were satisfactory. Such application had been created both for individual and collective sports, but in this last case, it only worked for soccer videos. Due to this, a new line of investigation arose, that consisted in the adaptation of this prototype to another sport such as basketball. Therefore, this Project has worked among the same lines, solving the existing issues and creating a similar prototype on which to continue investigation and further development. The system programming was adapted, in order to be able to be executed with a di erent number of inputted videos. Following this, the graphic interface has been modi ed, to be able to optimally represent the newly obtained results, maintaining the full functionality of the previous prototype. Furthermore, an improvement of the obtained results in these new videos was researched, with the system that was being implanted. The goal was to obtain the best possible results in both detection and tracking by adjusting di erent parameters. Lastly, due to the interface being the visible part of this project, there have been updates made to improve it, by including a new homography creation module, that simpli es the use by the end user even more

Shot classification in broadcast soccer video.

Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.Event understanding systems, responsible for automatically generating human relatable event descriptions from video sequences, is an open problem in computer vision research that has many applications in the sports domain, such as indexing and retrieval systems for sports video. Background modelling and shot classification of broadcast video are important steps in event understanding in video sequences. Shot classification seeks to identify shots, i.e. the labelling of continuous frame sequences captured by a single camera action such as long shot, close-up and audience shot, while background modelling seeks to classify pixels in an image as foreground/background. Many features used for shot classification are built upon the background model therefore background modelling is an essential part of shot classification. This dissertation reports on an investigation into techniques and procedures for background modelling and classification of shots in broadcast soccer videos. Broadcast video refers to video which would typically be viewed by a person at home on their television set and imposes constraints that are often not considered in many approaches to event detection. In this work we analyse the performances of two background modelling techniques appropriate for broadcast video, the colour distance model and Gaussian mixture model. The performance of the background models depends on correctly set parameters. Some techniques offer better updating schemes and thus adapt better to the changing conditions of a game, some are shown to be more robust to changes in broadcast technique and are therefore of greater value in shot classification. Our results show the colour distance model slightly outperformed the Gaussian mixture model with both techniques performing similar to those found in literature. Many features useful for shot classification are proposed in the literature. This dissertation identifies these features and presents a detailed analysis and comparison of various features appropriate for shot classification in broadcast soccer video. Once a feature set is established, a classifier is required to determine a shot class based on the extracted features. We establish the best use of the feature set and decision tree parameters that result in the best performance and then use a combined feature set to train a neural network to classify shots. The combined feature set in conjunction with the neural network classifier proved effective in classifying shots and in some situations outperformed those techniques found in literature

Análisis de actividad en vídeos deportivos multicámara

Los sistemas de análisis de contenido en vídeos deportivos están en continuo auge desde un punto de vista tanto comercial como investigador. A este respecto, se desarrolló en el grupo de procesamiento de vídeo (VPU-Lab) de la Escuela Politécnica Superior de la Universidad Autónoma de Madrid un prototipo de gestión de contenidos de vídeos deportivos con anterioridad a la realización de dicho proyecto. Este prototipo realizaba la detección y seguimiento de jugadores en vídeos deportivos, y sacaba algunos estadísticos de ellos. Dicho prototipo, con buenos resultados cuantitativos y cualitativos, presentaba una serie de de ciencias que motivaron la realización de este proyecto fin de carrera. Estas de ciencias estaban principalmente relacionadas con aspectos de usabilidad, interacción con el mismo, visualización de resultados y ajuste del funcionamiento. En este proyecto, se ha trabajado en dar solución a estos problemas en tres tareas fundamentales. En primer lugar, se trabajó en compactar el prototipo, anteriormente dividido en módulos que había que enlazar manualmente y en distintos lenguajes de programación. Al concluir el proyecto se dispone de un prototipo unificado en C++, con completa funcionalidad y portabilidad. En segundo lugar, se trató de mejorar tanto la usabilidad como la visualización e interacción con el mismo. Para ello se desarrollaron dos aplicaciones adaptadas a dos deportes concretos, fútbol y tenis, que permiten a un usuario no-experto, manejar el prototipo y visualizar los resultados del mismo, todo ello gracias a una Interfaz Gráfica de Usuario (GUI, Graphical User Interface). Por último, dado que, a la hora de comercializar estos prototipos, su funcionamiento en cliente suele ser supervisado, se ha dotado de herramientas al prototipo y a la interfaz para que sea posible una corrección del funcionamiento del algoritmo de manera online. Así un futuro supervisor puede interactuar con la aplicación y garantizar unos resultados muy superiores a los logrados por cualquier algoritmo automático.Sport video-content analysis systems are on the rise both from the commercial viewpoint and the researching viewpoint. In this scope, the video processing group (VPU-Lab) of Escuela Politécnica Superior, Universidad Autónoma de Madrid developed a prototype for sport video-content analysis previously to the beginning of this master thesis. This prototype performs the detection and tracking of players in sport videos, and provides statistical information about their behavior. This prototype achieved good results in quantitative and qualitative terms, presenting some de ciencies which motivate this mater thesis. These de ciencies were mainly related to aspects as usability, system interaction, results visualization and ne-tuning. This project has been focused in providing a solution for those problems by working on three main tasks. Firstly, the work focused in compacting the prototype. In origin it was divided in modules which have to bee manually linked and which are programmed in di erent languages. As a result of the project there is an uni ed prototype fully programmed in C++, full working and portable. Secondly, e orts were aimed to improve the usability, interactions and results visualization of the prototype. Two applications were developed and adapted to guarantee speci c sports support, football and tennis. They allow a non-expert user to fully control the prototype and visually obtain its results, via a Graphical User Interface (GIU). Finally, keeping in mind that this products use to work under supervision in commercial applications, the prototype and the interface have been equipped with tools to allow the online interaction with its results. This improvement allows a supervisor to control the application and correct its results when necessary, obtaining more reliable results than any other automatic system

Table tennis event detection and classification

It is well understood that multiple video cameras and computer vision (CV) technology can be used in sport for match officiating, statistics and player performance analysis. A review of the literature reveals a number of existing solutions, both commercial and theoretical, within this domain. However, these solutions are expensive and often complex in their installation. The hypothesis for this research states that by considering only changes in ball motion, automatic event classification is achievable with low-cost monocular video recording devices, without the need for 3-dimensional (3D) positional ball data and representation. The focus of this research is a rigorous empirical study of low cost single consumer-grade video camera solutions applied to table tennis, confirming that monocular CV based detected ball location data contains sufficient information to enable key match-play events to be recognised and measured. In total a library of 276 event-based video sequences, using a range of recording hardware, were produced for this research. The research has four key considerations: i) an investigation into an effective recording environment with minimum configuration and calibration, ii) the selection and optimisation of a CV algorithm to detect the ball from the resulting single source video data, iii) validation of the accuracy of the 2-dimensional (2D) CV data for motion change detection, and iv) the data requirements and processing techniques necessary to automatically detect changes in ball motion and match those to match-play events. Throughout the thesis, table tennis has been chosen as the example sport for observational and experimental analysis since it offers a number of specific CV challenges due to the relatively high ball speed (in excess of 100kph) and small ball size (40mm in diameter). Furthermore, the inherent rules of table tennis show potential for a monocular based event classification vision system. As the initial stage, a proposed optimum location and configuration of the single camera is defined. Next, the selection of a CV algorithm is critical in obtaining usable ball motion data. It is shown in this research that segmentation processes vary in their ball detection capabilities and location out-puts, which ultimately affects the ability of automated event detection and decision making solutions. Therefore, a comparison of CV algorithms is necessary to establish confidence in the accuracy of the derived location of the ball. As part of the research, a CV software environment has been developed to allow robust, repeatable and direct comparisons between different CV algorithms. An event based method of evaluating the success of a CV algorithm is proposed. Comparison of CV algorithms is made against the novel Efficacy Metric Set (EMS), producing a measurable Relative Efficacy Index (REI). Within the context of this low cost, single camera ball trajectory and event investigation, experimental results provided show that the Horn-Schunck Optical Flow algorithm, with a REI of 163.5 is the most successful method when compared to a discrete selection of CV detection and extraction techniques gathered from the literature review. Furthermore, evidence based data from the REI also suggests switching to the Canny edge detector (a REI of 186.4) for segmentation of the ball when in close proximity to the net. In addition to and in support of the data generated from the CV software environment, a novel method is presented for producing simultaneous data from 3D marker based recordings, reduced to 2D and compared directly to the CV output to establish comparative time-resolved data for the ball location. It is proposed here that a continuous scale factor, based on the known dimensions of the ball, is incorporated at every frame. Using this method, comparison results show a mean accuracy of 3.01mm when applied to a selection of nineteen video sequences and events. This tolerance is within 10% of the diameter of the ball and accountable by the limits of image resolution. Further experimental results demonstrate the ability to identify a number of match-play events from a monocular image sequence using a combination of the suggested optimum algorithm and ball motion analysis methods. The results show a promising application of 2D based CV processing to match-play event classification with an overall success rate of 95.9%. The majority of failures occur when the ball, during returns and services, is partially occluded by either the player or racket, due to the inherent problem of using a monocular recording device. Finally, the thesis proposes further research and extensions for developing and implementing monocular based CV processing of motion based event analysis and classification in a wider range of applications