A fog based ball tracking (FB2T) system using intelligent ball bees

Abulwafa, Arwa E.; Saleh, Ahmed I.; Ali, Hesham; Saraya, Mohamed S.

doi:10.1007/s12652-020-01948-6

Cited by 8 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are two main methodological tracks: i) using broadcast videos and ii) having a fixed camera setup in the stadium. More recently, the use of drones [12] has also been suggested, yet it is not as common. Several studies focus on the detection of the ball only, whereas others also propose a trajectory for the ball.…”

Section: Related Workmentioning

confidence: 99%

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

AMİRLİ

Alemdar

2022

Academic Platform Journal of Engineering and Smart Systems

View full text Add to dashboard Cite

Tracking the ball location is essential for automated game analysis in complex ball-centered team sports such as football. However, it has always been a challenge for image processing-based techniques because the players and other factors often occlude the view of the ball. This study proposes an automated machine learning-based method for predicting the ball location from players' behavior on the pitch. The model has been built by processing spatial information of players acquired from optical tracking data. Optical tracking data include samples from 300 matches of the 2017-2018 season of the Turkish Football Federation's Super League. We use neural networks to predict the ball location in 2D axes. The average coefficient of determination of the ball tracking model on the test set both for the x-axis and the y-axis is accordingly 79% and 92%, where the mean absolute error is 7.56 meters for the x-axis and 5.01 meters for the y-axis.

show abstract

Section: Related Workmentioning

confidence: 99%

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

AMİRLİ

Alemdar

2022

Academic Platform Journal of Engineering and Smart Systems

View full text Add to dashboard Cite

show abstract

“…Further, the literature [11] also selects the videos of the best athletes with the most similar scores to the trainees for each sport characteristic and recommends them to the trainees as learning targets. In addition, the literature [12] provides a conceptualization on how to better monitor and analyze the data, respectively, including how to choose the type of data to be monitored in sports and how to process the data after monitoring, which provides a guideline for digitization of general sports training. The literature [13] uses an inverse reinforcement learning model to learn basic stroke strategies in table tennis, and a reward model (REM) obtained by training from sports data can extract key information (e.g., speed, and landing point) in high-level strokes.…”

Section: Related Workmentioning

confidence: 99%

Mathematical Modeling and Simulation of Table Tennis Trajectory Based on Digital Video Image Processing

Zhang

Gui

et al. 2021

Advances in Mathematical Physics

View full text Add to dashboard Cite

Digital sports training based on digital video image processing promises to reduce the reliance on the experience of coaches in the table tennis training process and to achieve a more general physical education base. Based on this approach, this paper describes the specific forms of exercise content, movement characteristics, and skill levels in the table tennis framework and specifies the calculation methods of motion capture and movement characteristics suitable for table tennis. Meanwhile, to further improve the accuracy of the inertial motion capture system in restoring the position posture of the trainees, this paper improves the original inertial motion capture system from two aspects: contact judgment of both feet and correction of the position posture based on the contact position constraint. The simulation results show that the corrected human posture has good action smoothness. This paper first proposes a knowledge-based generic sports-assisted training framework based on generalizing the traditional sports training model. The framework contains four main modules: domain knowledge, trainees, sport evaluation, and controller. The domain knowledge module is a digital representation of the knowledge of the exercise content, improvement instructions, and skill indicators of the sport; the trainee module is the active response of the trainee to the exercise content and improvement instructions; the motion evaluation module uses motion capture technology to obtain the raw motion data of the trainee and further calculates the motion characteristics; the controller module proposes improvement instructions to the trainee or makes him/her practice new content based on the results of the motion evaluation. Based on the results of the motion evaluation, the controller module proposes improvement instructions or makes the trainee practice new content until the trainee achieves the desired goal.

show abstract

“…The most commonly used F-measure in text classification is F1 (i.e., n =1), which weights equally both precision and recall. It can be expressed in (13).…”

Section: 1performance Metricsmentioning

confidence: 99%

“…[22][23][24] The last approach that can be used for ball tracking is B 2 A, which utilizes a set of cooperative ball bees for tracking the ball across the football court. Abulwafa et al 12 introduced it, where a quadcopter, called the ball bee, has been used for tracking the soccer ball. The camera of this bee is a moving camera fixed in the bottom body of it; also, the photos taken by this camera are high-quality photos.…”

Section: Camera Setup For Ball Detection and Trackingmentioning

confidence: 99%

See 1 more Smart Citation

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

Abulwafa

Saleh

Saraya

et al. 2021

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

Sports video analysis has received much attention as it is turned to be a hot research area in the field of image processing. This led to opportunities to develop fascinating applications supported by analysis of different sports especially football. Identifying the ball in soccer images is an essential task for not only goal scoring but also players' evaluation. However, soccer ball detection suffers from several hurdles such as; occlusions, fast moving objects, shadows, poor lighting, color contrast, and other static background objects. Although several ball detection techniques have been introduced such as; Frame Difference, Mixture of Gaussian (MoG), Optical Flow and etc., ball detection in soccer games is still an open research area. In this paper, a new Fuzzy Based Ball Detection (FB 2 D) strategy is proposed for identifying the ball through a set of image sequences extracted form a soccer match video. FB 2 D has the ability to accurately identify the ball even if it is attached to the white lines drawn on the playground or partially occluded behind players. FB 2 D has been compared to recent ball detection techniques. Experimental results have shown that FB 2 D outperforms recent detection techniques as it introduced the maximum accuracy and the accuracy of detection in the testing stage is close to 100%. As well as the minimum error.

show abstract

A fog based ball tracking (FB2T) system using intelligent ball bees

Cited by 8 publications

References 28 publications

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Mathematical Modeling and Simulation of Table Tennis Trajectory Based on Digital Video Image Processing

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

Contact Info

Product

Resources

About