Spatial movement pattern recognition in soccer based on relative player movements

Knowledge of spatial movement patterns in soccer occurring on a regular basis can give a soccer coach, analyst or reporter insights in the playing style or tactics of a group of players or team. Furthermore, it can support a coach to better prepare for a soccer match by analysing (trained) movement patterns of both his own as well as opponent players. We explore the use of the Qualitative Trajectory Calculus (QTC), a spatiotemporal qualitative calculus describing the relative movement between objects, for spatial movement pattern recognition of players movements in soccer. The proposed method allows for the recognition of spatial movement patterns that occur on different parts of the field and/or at different spatial scales. Furthermore, the Levenshtein distance metric supports the recognition of similar movements that occur at different speeds and enables the comparison of movements that have different temporal lengths. We first present the basics of the calculus, and subsequently illustrate its applicability with a real soccer case. To that end, we present a situation where a user chooses the movements of two players during 20 seconds of a real soccer match of a 2016-2017 professional soccer competition as a reference fragment. Following a pattern matching procedure, we describe all other fragments with QTC and calculate their distance with the QTC representation of the reference fragment. The top-k most similar fragments of the same match are presented and validated by means of a duo-trio test. The analyses show the potential of QTC for spatial movement pattern recognition in soccer

SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos

In this paper, we introduce SoccerNet, a benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). As such, the dataset is easily scalable. These annotations are manually refined to a one second resolution by anchoring them at a single timestamp following well-defined soccer rules. With an average of one event every 6.9 minutes, this dataset focuses on the problem of localizing very sparse events within long videos. We define the task of spotting as finding the anchors of soccer events in a video. Making use of recent developments in the realm of generic action recognition and detection in video, we provide strong baselines for detecting soccer events. We show that our best model for classifying temporal segments of length one minute reaches a mean Average Precision (mAP) of 67.8%. For the spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances $\delta$ ranging from 5 to 60 seconds. Our dataset and models are available at https://silviogiancola.github.io/SoccerNet.Comment: CVPR Workshop on Computer Vision in Sports 201

TVCalib: Camera Calibration for Sports Field Registration in Soccer

Sports field registration in broadcast videos is typically interpreted as the task of homography estimation, which provides a mapping between a planar field and the corresponding visible area of the image. In contrast to previous approaches, we consider the task as a camera calibration problem. First, we introduce a differentiable objective function that is able to learn the camera pose and focal length from segment correspondences (e.g., lines, point clouds), based on pixel-level annotations for segments of a known calibration object. The calibration module iteratively minimizes the segment reprojection error induced by the estimated camera parameters. Second, we propose a novel approach for 3D sports field registration from broadcast soccer images. Compared to the typical solution, which subsequently refines an initial estimation, our solution does it in one step. The proposed method is evaluated for sports field registration on two datasets and achieves superior results compared to two state-of-the-art approaches.Comment: Accepted for publication at WACV'2

A Novel and Effective Short Track Speed Skating Tracking System

This dissertation proposes a novel and effective system for tracking high-speed skaters. A novel registration method is employed to automatically discover key frames to build the panorama. Then, the homography between a frame and the real world rink can be generated accordingly. Aimed at several challenging tracking problems of short track skating, a novel multiple-objects tracking approach is proposed which includes: Gaussian mixture models (GMMs), evolving templates, constrained dynamical model, fuzzy model, multiple templates initialization, and evolution. The outputs of the system include spatialtemporal trajectories, velocity analysis, and 2D reconstruction animations. The tracking accuracy is about 10 cm (2 pixels). Such information is invaluable for sports experts. Experimental results demonstrate the effectiveness and robustness of the proposed system

Efectos de la implementación de un programa de entrenamiento en variabilidad sobre la precisión del saque de esquina en jóvenes futbolistas

En los últimos años han surgido diferentes trabajos que determinan como clave la variabilidad motora a la hora del aprendizaje técnico en diversos deportes. Estudios recientes han tratado de manipular la variabilidad motora mediante la práctica con el fin de maximizar el aprendizaje para que sea mucho más diferencial. En el presente trabajo final de máster, se busca determinar cómo afecta un programa de entrenamiento basado en la práctica en variabilidad en una habilidad cerrada como es saque de esquina en fútbol. Dieciocho participantes ejecutaron un total de cuatro lanzamientos de saque de esquina correspondientes a dos de cada lado en lo que se denomina como una situación puramente estándar. Se analizaron los lanzamientos de esquina con el software Kinovea para comprobar el rango de precisión en base a la literatura científica sobre las zonas con mayor eficacia en esta habilidad. Tras determinar la precisión inicial de los lanzamientos, se implementó un programa de entrenamiento basado en la variabilidad motora durante los golpeos con el fin de mejorar la precisión de los lanzamientos. El programa siguió una secuencia basada en un incremento progresivo de la carga y posteriormente se evaluó el rendimiento alcanzado tras el mismo. Además, se realizaron dos test de retención para comprobar el efecto de retención tras la intervención realizada.In recent years, different studies have emerged that identify motor variability as key to technical learning in various sports. Recent studies have tried to manipulate motor variability through practice in order to maximise learning so that it is much more differential. In this master's thesis, we sought to determine how a practice-based training programme affects variability in a closed skill such as the corner kick in football. Eighteen participants performed a total of four corner kicks corresponding to two on each side in what is referred to as a purely standard situation. The corner kicks were analysed using Kinovea software to check the range of accuracy based on the scientific literature on the most effective areas for this skill. After determining the initial accuracy of the throws, a training programme based on motor variability during the strikes was implemented in order to improve the accuracy of the throws. The programme followed a sequence based on a progressive increase in load and the performance achieved after the programme was evaluated. In addition, two retention tests were carried out to check the retention effect after the intervention