A technology platform for automatic high-level tennis game analysis

Sports video research is a popular topic that has been applied to many prominent sports for a large spectrum of applications. In this paper we introduce a technology platform which has been developed for the tennis context, able to extract action sequences and provide support to coaches for players performance analysis during training and official matches. The system consists of an hardware architecture, devised to acquire data in the tennis context and for the specific domain requirements, and a number of processing modules which are able to track both the ball and the players, to extract semantic information from their interactions and automatically annotate video sequences. The aim of this paper is to demonstrate that the proposed combination of hardware and software modules is able to extract 3D ball trajectories robust enough to evaluate ball changes of direction recognizing serves, strokes and bounces. Starting from these information, a finite state machine based decision process can be employed to evaluate the score of each action of the game. The entire platform has been tested in real experiments during both training sessions and matches, and results show that automatic annotation of key events along with 3D positions and scores can be used to support coaches in the extraction of valuable information about players intentions and behaviours

Graph-Based Multi-Camera Soccer Player Tracker

The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field. The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions relying on the appearance of tracked objects. Our method focuses on individual player dynamics and interactions between neighborhood players to improve tracking performance. To overcome the difficulty of reliably merging detections from multiple cameras in the presence of calibration errors, we propose the novel tracking approach, where the tracker operates directly on raw detection heat maps from multiple cameras. Our model is trained on a large synthetic dataset generated using Google Research Football Environment and fine-tuned using real-world data to reduce costs involved with ground truth preparation

Development of a tracking system using invisible markers for association football

Mestrado em Treino DesportivoNowadays in association football, obtaining information such as position and movements of the players is of great interest to coaches due to the potential to relate performance to tactics and to assist in planning training programs. Over the last decade, technological advances in this area included the introduction of more sophisticated systems that are being used in elite association football; however, the development of a fully automated system is still needed. The aim of this study is to contribute to the development of a non-intrusive, automatic, tracking system, using a marker invisible to humans. We select a marker that absorbs in the infrared region (IR) of the electromagnetic spectrum (Epolight 1110) and prepared solutions containing the marker. We tested the solutions in fabric samples to assess the tones of gray, as well as the resistance of the marker to water. T-shirts were soaked in the solutions created, and were used by football players in a 1vs2 in situ task where we proceeded to the tracking. The findings showed that this approach is a valid possibility to discriminate and track players. We concluded that it is possible to use IR markers to distinguish different players and, with the appropriate computer graphics’ algorithms, to automatically track the players.Actualmente no futebol, a obtenção de informações como posição e movimentos dos jogadores é de grande interesse por parte dos técnicos devido ao potencial para relacionar o desempenho à táctica e ajudar na elaboração dos programas de treino. Durante a última década, avanços tecnológicos nesta área incluíram a introdução de sistemas mais sofisticados que estão a ser utilizados no futebol de elite; no entanto, o desenvolvimento de um sistema totalmente automatizado ainda é necessário. O objectivo deste estudo é contribuir para o desenvolvimento de um sistema de tracking não-intrusivo, automático, usando um marcador invisível para os seres humanos. Seleccionámos um marcador que absorve na região do infravermelho (IV) do espectro electromagnético (Epolight 1110) e preparámos soluções contendo o marcador. Testámos as soluções em amostras de tecido para avaliar os tons de cinzento, assim como a resistência do marcador à água. Foram embebidas T-shirts com as soluções criadas, que foram usadas por jogadores de futebol numa situação in situ de 1vs2 onde se procedeu ao tracking dos mesmos. Os resultados mostraram que esta abordagem é válida para discriminar e acompanhar os jogadores. Concluímos que é possível usar marcadores IV para distinguir diferentes jogadores e, com algoritmos de computação gráfica adequados, é possível monitorizar automaticamente os jogadores

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

Tracking of a Basketball Using Multiple Cameras

Projecte final de carrera fet en copl.laboració amb École Polytechnique Fédérale de LaussanneThis master thesis presents a method for tracking a basketball during a basketball match recorded with a multi-camera system. We first developed methods to detect a ball in images based on its appearance. Color was used through a color histogram of the ball, manually initialized with ball samples. Then the shape of the ball was used in two different ways: by analyzing the circularity of the ball contour and by using the Hough transform to find circles in the image. In a second step, we attempted to track the ball in three dimensions using the cameras calibration, as well as the image methods previously developed. Using a recursive tracking procedure, we define a 3-dimensional search volume around the previously known position of the ball and evaluate the presence of a ball in all candidate positions inside this volume. This is performed by projecting the candidate positions in all camera views and checking the ball presence using color and shape cues. Extrapolating the future position of the ball based on its movements in the past frames was also tested to make our method more robust to motion blur and occlusions. Evaluation of the proposed algorithm has been done on a set of synchronized multi-camera sequences. The results have shown that the algorithm can track the ball and find its 3D position during several consecutive frames

El uso de la tecnología de captura de movimiento para el análisis del rendimiento deportivo

In sport performance, motion capture aims at tracking and recording athletes’ human motion in real time to analyze physical condition, athletic performance, technical expertise and injury mechanism, prevention and rehabilitation. The aim of this paper is to systematically review the latest developments of motion capture systems for the analysis of sport performance. To that end, selected keywords were searched on studies published in the last four years in the electronic databases ISI Web of Knowledge, Scopus, PubMed and SPORTDiscus, which resulted in 892 potential records. After duplicate removal and screening of the remaining records, 81 journal papers were retained for inclusion in this review, distributed as 53 records for optical systems, 15 records for non-optical systems and 13 records for markerless systems. Resultant records were screened to distribute them according to the following analysis categories: biomechanical motion analysis, validation of new systems and performance enhancement. Although optical systems are regarded as golden standard with accurate results, the cost of equipment and time needed to capture and postprocess data have led researchers to test other technologies. First, non-optical systems rely on attaching sensors to body parts to send their spatial information to computer wirelessly by means of different technologies, such as electromagnetic and inertial (accelerometry). Finally, markerless systems are adequate for free, unobstructive motion analysis since no attachment is carried by athletes. However, more sensors and sophisticated signal processing must be used to increase the expected level of accuracy.En el ámbito del rendimiento deportivo, el objetivo de la captura de movimiento es seguir y registrar el movimiento humano de deportistas para analizar su condición física, rendimiento, técnica y el origen, prevención y rehabilitación de lesiones. En este artículo, se realiza una revisión sistemática de los últimos avances en sistemas de captura de movimiento para el análisis del rendimiento deportivo. Para ello, se buscaron palabras clave en estudios publicados en los últimos cuatro años en las bases de datos electrónicas ISI Web of Knowledge, Scopus, PubMed y SPORTDiscus, dando lugar a 892 registros. Tras borrar duplicados y análisis del resto, se seleccionaron 81 artículos de revista, distribuidos en 53 registros para sistemas ópticos, 15 para sistemas no ópticos y 13 para sistemas sin marcadores. Los registros se clasificaron según las categorías: análisis biomecánico, validación de nuevos sistemas y mejora del rendimiento. Aunque los sistemas ópticos son los sistemas de referencia por su precisión, el coste del equipamiento y el tiempo invertido en la captura y postprocesado ha llevado a los investigadores a probar otras tecnologías. En primer lugar, los sistemas no ópticos se basan en adherir sensores a zonas corporales para mandar su información espacial a un ordenador mediante distintas tecnologías, tales como electromagnética y inercial (acelerometría). Finalmente, los sistemas sin marcadores permiten un análisis del movimiento sin restricciones ya que los deportistas no llevan adherido ningún elemento. Sin embargo, se necesitan más sensores y un procesado de señal avanzado para aumentar el nivel de precisión necesario

From 2D leg kinematics to 3D full-body biomechanics-the past, present and future of scientific analysis of maximal instep kick in soccer

Biomechanics investigation on soccer kicking has a relatively long history, yet the body of knowledge is still small. This paper reviews articles published from 1960s to 2011, summarizing relevant findings, research trends and method development. It also discusses challenges faced by the field. The main aim of the paper is to promote soccer kicking studies through discussions on problem solving in the past, method development in the present, and possible research directions for the future