Optical tracking in team sports

Abstract

Sports analysis has gained paramount importance for coaches, scouts, and fans. Recently, computer vision researchers have taken on the challenge of collecting the necessary data by proposing several methods of automatic player and ball tracking. Building on the gathered tracking data, data miners are able to perform quantitative analysis on the performance of players and teams. With this survey, our goal is to provide a basic understanding for quantitative data analysts about the process of creating the input data and the characteristics thereof. Thus, we summarize the recent methods of optical tracking by providing a comprehensive taxonomy of conventional and deep learning methods, separately. Moreover, we discuss the preprocessing steps of tracking, the most common challenges in this domain, and the application of tracking data to sports teams. Finally, we compare the methods by their cost and limitations, and conclude the work by highlighting potential future research directions.

A goal-aligned coordinate system for invasion games

“…Spatio-temporal data is essential for sports analytics (Torres-Ronda et al, 2022;Memmert & Raabe, 2018;Zuccolotto & Manisera, 2020;Shea et al, 2017) and considerable effort is invested to achieve high levels of accuracy and precision (Linke et al, 2018;Rahimian & Toka, 2022). It appears that the seemingly straightforward specification of a data format convention has received comparatively less attention.…”

The Eye in the Sky—A Method to Obtain On-Field Locations of Australian Rules Football Athletes

“…Performance analysts in professional sports teams are increasingly required to analyse large sets of athlete data to derive insights that result in a competitive advantage over the opposition [1]. The in-depth analysis of team sports is on the rise due to advancements in sensor technology and computing power [2][3][4]. The first step of all analyses is the accurate tracking of players on the field.…”

“…The first step of all analyses is the accurate tracking of players on the field. This can be achieved by sensor data such as GPS, LPS, or IMUs, or using video [3]. The notion of tracking data refers to spatiotemporal data describing ball and/or player positions during a sports event [5].…”

“…The major challenges of vision-based methods are their dependency on the environmentthey are susceptible to frequent changes in athlete velocities and occlusions in congested play, changes in field lighting, and similarities in the appearances of teammates [2,3]. Further, the unique challenge of varying field sizes in combination with the large field size requiring multiple cameras makes the use of conventional player tracking methods impossible [4].…”

Conceptual structure and current trends in Artificial Intelligence, Machine Learning, and Deep Learning research in sports: A bibliometric review

“…Regarding the basic review situation, several works have been published to date in the context of AI and sport-specific fields of its application [21,[32][33][34][35]. In addition to AI topics, numerous general works can be found in the context of sports using bibliometric methods (e.g., football [36][37][38]; sports tourism [39,40]; sports sustainability [41]; sports management [42]; sports nutrition [43]; sports science in general [44]).…”