BALL TRACKING IN FOOTBALL

This study outines the evaluation of VICON TRACKER software in tracking a ball in a large test area. A 22 camera VICON system and VICON Tracker software was used to track a soccer ball both indoor (22 m x 12 m) and outdoor (22 m x 28 m). Different ball marker setups were evaluated for accuracy and percentage data capture manipulating the number and type of reflective markers, as well as software settings, ands whether the system identified the centre of the ball. TRACKER achieved greater than 90% data capture percentage using the half dome markers or nine flat marker setups. Importantly a pre-test setup of the ball object and a 200 Hz (as opposed to 100 Hz) sample rate was needed. The ball centre was also captured by the software. Future work will examine if this system can perform in larger spaces and with multiple players in the area

Towards Active Learning for Action Spotting in Association Football Videos

Association football is a complex and dynamic sport, with numerous actions occurring simultaneously in each game. Analyzing football videos is challenging and requires identifying subtle and diverse spatio-temporal patterns. Despite recent advances in computer vision, current algorithms still face significant challenges when learning from limited annotated data, lowering their performance in detecting these patterns. In this paper, we propose an active learning framework that selects the most informative video samples to be annotated next, thus drastically reducing the annotation effort and accelerating the training of action spotting models to reach the highest accuracy at a faster pace. Our approach leverages the notion of uncertainty sampling to select the most challenging video clips to train on next, hastening the learning process of the algorithm. We demonstrate that our proposed active learning framework effectively reduces the required training data for accurate action spotting in football videos. We achieve similar performances for action spotting with NetVLAD++ on SoccerNet-v2, using only one-third of the dataset, indicating significant capabilities for reducing annotation time and improving data efficiency. We further validate our approach on two new datasets that focus on temporally localizing actions of headers and passes, proving its effectiveness across different action semantics in football. We believe our active learning framework for action spotting would support further applications of action spotting algorithms and accelerate annotation campaigns in the sports domain.Comment: Accepted at CVSports'2

Repeatability of a piezoelectric force platform to measure impact metrics for a single model of football

The visco-elastic properties of a football influence how it bounces and therefore its performance in a game. Previously, high-speed camera footage has been used to quantify deformation, coefficient of restitution and contact time for an impact between a football and a rigid surface but these systems do not provide any information on the forces acting on the football during the impact. The aim of this study was to determine the repeatability of measuring the peak impact force, impulse, rise time and loading rate for four samples of the same model of football using a commercial force platform (Kistler 9281EA). A football impacted the floor-mounted piezoelectric-type force platform at 6.04 and 19.4 m s−1. High absolute (coefficient of variation (CV) ≤ 10%) and relative (intraclass correlation coefficient (ICC) ≥ 0.94) repeatability was observed for repeated impacts at both velocities. The minimal detectable differences were calculated to evaluate the ability for the force platform to be used to make meaningful comparisons between footballs. For all metrics, the minimum detectable difference accounted for less than 5% of the mean value. Therefore, it can be concluded that provided the difference in impact metrics between football models exceeds the minimal detectable difference, the commercial force platform can be used to measure and detect differences in physical impact metrics between models of footballs

Challenges and considerations in determining the quality of electronic performance & tracking systems for team sports

Electronic performance & tracking systems (EPTS) are commonly used to track the location and velocity of athletes in many team sports. A range of associated applications using the derived data exist, such as assessment of athlete characteristics, informing training design, assisting match adjudication and providing fan insights for broadcast. Consequently the quality of such systems is of importance to a range of stakeholders. The influence of both systematic and methodological factors such as hardware, software settings, sample rate and filtering on this resulting quality is non-trivial. Highlighting these allows for the user to understand their strengths and limitations in various decision-making processes, as well as identify areas for research and development. In this paper, a number of challenges and considerations relating to the determination of EPTS validity for team sport are outlined and discussed. The aim of this paper is to draw attention of these factors to both researchers and practitioners looking to inform their decision-making in the EPTS area. Addressing some of the posited considerations in future work may represent best practice; others may require further investigation, have multiple potential solutions or currently be intractable

Challenges and considerations in determining the quality of electronic performance & tracking systems for team sports

Electronic performance & tracking systems (EPTS) are commonly used to track the location and velocity of athletes in many team sports. A range of associated applications using the derived data exist, such as assessment of athlete characteristics, informing training design, assisting match adjudication and providing fan insights for broadcast. Consequently the quality of such systems is of importance to a range of stakeholders. The influence of both systematic and methodological factors such as hardware, software settings, sample rate and filtering on this resulting quality is non-trivial. Highlighting these allows for the user to understand their strengths and limitations in various decision-making processes, as well as identify areas for research and development. In this paper, a number of challenges and considerations relating to the determination of EPTS validity for team sport are outlined and discussed. The aim of this paper is to draw attention of these factors to both researchers and practitioners looking to inform their decision-making in the EPTS area. Addressing some of the posited considerations in future work may represent best practice; others may require further investigation, have multiple potential solutions or currently be intractable

Automatic event detection in football using tracking data

Abstract One of the main shortcomings of event data in football, which has been extensively used for analytics in the recent years, is that it still requires manual collection, thus limiting its availability to a reduced number of tournaments. In this work, we propose a deterministic decision tree-based algorithm to automatically extract football events using tracking data, which consists of two steps: (1) a possession step that evaluates which player was in possession of the ball at each frame in the tracking data, as well as the distinct player configurations during the time intervals where the ball is not in play to inform set piece detection; (2) an event detection step that combines the changes in ball possession computed in the first step with the laws of football to determine in-game events and set pieces. The automatically generated events are benchmarked against manually annotated events and we show that in most event categories the proposed methodology achieves $$+90\%$$ + 90 % detection rate across different tournaments and tracking data providers. Finally, we demonstrate how the contextual information offered by tracking data can be leveraged to increase the granularity of auto-detected events, and exhibit how the proposed framework may be used to conduct a myriad of data analyses in football