Tracking football player movement from a single moving camera using particle filters

This paper deals with the problem of tracking football players in a football match using data from a single moving camera. Tracking footballers from a single video source is difficult: not only do the football players occlude each other, but they frequently enter and leave the cameras field of view, making initialisation and destruction of a players tracking a difficult task. The system presented here uses particle filters to track players. The multiple state estimates used by a particle filter provide an elegant method for maintaining tracking of players following an occlusion. Automated tracking can be achieved by creating and stopping particle filters depending on the input player data

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

The role of motion analysis in elite soccer

The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game especially due to the increased physical demands of match-play. The monitoring of players’ work-rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university- based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, identify practical implications of the established body of knowledge, while highlighting areas that require further exploration

Survey on Vision-based Path Prediction

Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.Comment: DAPI 201

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

An automatic visual analysis system for tennis

This article presents a novel video analysis system for coaching tennis players of all levels, which uses computer vision algorithms to automatically edit and index tennis videos into meaningful annotations. Existing tennis coaching software lacks the ability to automatically index a tennis match into key events, and therefore, a coach who uses existing software is burdened with time-consuming manual video editing. This work aims to explore the effectiveness of a system to automatically detect tennis events. A secondary aim of this work is to explore the bene- fits coaches experience in using an event retrieval system to retrieve the automatically indexed events. It was found that automatic event detection can significantly improve the experience of using video feedback as part of an instructional coaching session. In addition to the automatic detection of key tennis events, player and ball movements are automati- cally tracked throughout an entire match and this wealth of data allows users to find interesting patterns in play. Player and ball movement information are integrated with the automatically detected tennis events, and coaches can query the data to retrieve relevant key points during a match or analyse player patterns that need attention. This coaching software system allows coaches to build advanced queries, which cannot be facilitated with existing video coaching solutions, without tedious manual indexing. This article proves that the event detection algorithms in this work can detect the main events in tennis with an average precision and recall of 0.84 and 0.86, respectively, and can typically eliminate man- ual indexing of key tennis events

Learning models of camera control for imitation in football matches

In this paper, we present ongoing work towards a system capable of learning from and imitating the movement of a trained cameraman and his director covering a football match. Useful features such as the pitch and the movement of players in the scene are detected using various computer vision techniques. In simulation, a robotic camera trains its own internal model for how it can affect these features. The movement of a real cameraman in an actual football game can be imitated by using this internal model