Low-Cost Optical Tracking of Soccer Players

Csanalosi, Gabor; Dobreff, Gergely; Pašić, Alija; Molnár, Márton; Toka, László

doi:10.1007/978-3-030-64912-8_3

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Detection methods: Originally, feature maps associated with classifiers are used to identify the players, such as the Deformable Part Model [7,8] and the histogram of oriented gradients (HOG) features [9]. Gaussian mixture model (GMM) can separate background and foreground [10]. Those methods work well for offline processing but depend on pre-selected features to identify the objects.…”

Section: Related Workmentioning

confidence: 99%

Low Cost Player Tracking in Field Hockey

Moura

Kholkine

Damme

et al. 2022

Communications in Computer and Information Science

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In the paper, we describe the technical details of a multiplayer tracker system using tracking data obtained from a single low-cost stationary camera on field hockey games. Analyzing the tracking data of the players only from the transmitted video opens a multitude of applications that allows the cost of technology to be reduced. This method does not depend on the cooperation of the players (by using sensors) or their teams (by sharing data with a third party). The approach taken in this paper uses a variety of computer vision and tracking techniques. Making player tracking data more accessible lowers the barrier to entry for sports research and increases the period during which advanced analysis methods can be applied. The proposed system runs the full pipeline at 3 fps on a computer with a simple graphics card.

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Section: Related Workmentioning

confidence: 99%

Low Cost Player Tracking in Field Hockey

Moura

Kholkine

Damme

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

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Optimizing and dimensioning a data intensive cloud application for soccer player tracking

Dobreff

Molnár

Toka

2022

International Journal of Computer Science in Sport

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Cloud-based services revolutionize how applications are designed and provisioned in more and more application domains. Operating a cloud application, however, requires careful choices of configuration settings so that the quality of service is acceptable at all times, while cloud costs remain reasonable. We propose an analytical queuing model for cloud resource provisioning that provides an approximation on end-to-end application latency and on cloud resource usage, and we evaluate its performance. We pick an emerging use case of cloud deployment for validation: sports analytics. We have created a low-cost, cloud-based soccer player tracking system. We present the optimization of the cloud-deployed data processing of this system: we set the parameters with the aim of sacrificing as least as possible on accuracy, i.e., quality of service, while keeping latency and cloud costs low. We demonstrate that the analytical model we propose to estimate the end-to-end latency of a microservice-type cloud native application falls within a close range of what the measurements of the real implementation show. The model is therefore suitable for the planning of the cloud deployment costs for microservice-type applications as well.

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Low-Cost Optical Tracking of Soccer Players

Cited by 2 publications

References 9 publications

Low Cost Player Tracking in Field Hockey

Low Cost Player Tracking in Field Hockey

Optimizing and dimensioning a data intensive cloud application for soccer player tracking

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