Markerless Tracking of Tennis Racket Motion Using a Camera

Elliott, Nathan; Choppin, Simon; Goodwill, Simon; Allen, Tom

doi:10.1016/j.proeng.2014.06.060

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…The calibration with the orthogonal object was performed once as it remained stationary along with the master camera, reducing uncertainty from manual digitisation in comparison to digitising the control points in an image from each slave camera position. Using an orthogonal object improved camera pose accuracy, in comparison to simply using the Perspex board [25] or racket as a planar calibration object, as detailed by Elliott [34]. MATLAB's [32] image processing toolbox was used to perform thresholding to digitally extract racket silhouettes from the slave camera's images and to segment polygonal silhouette boundaries [37][38][39][40][41].…”

Section: Construction Of a Calibrated Set Of Silhouette Viewsmentioning

confidence: 99%

“…More regular shaped particles can appear similar from different viewpoints, and therefore, the optimisation routine is more likely to converge to a local minimum away from the solution [21]. Elliott et al [25] adapted methods from other authors [26,27] to produce a 44-view calibrated set of a 1:5 scale model tennis racket using one camera. Single view fitting techniques adopted from Price and Morrison [21] were applied to estimate racket position to within ±2 mm.…”

Section: Introductionmentioning

confidence: 99%

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Single view silhouette fitting techniques for estimating tennis racket position

et al. 2017

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Stereo camera systems have been used to track markers attached to a racket, allowing its position to be obtained in three-dimensional (3D) space. Typically, markers are manually selected on the image plane, but this can be time-consuming. A markerless system based on one stationary camera estimating 3D racket position data is desirable for research and play. The markerless method presented in this paper relies on a set of racket silhouette views in a common reference frame captured with a calibrated camera and a silhouette of a racket captured with a camera whose relative pose is outside the common reference frame. The aim of this paper is to provide validation of these single view fitting techniques to estimate the pose of a tennis racket. This includes the development of a calibration method to provide the relative pose of a stationary camera with respect to a racket. Mean static racket position was reconstructed to within ±2 mm. Computer generated camera poses and silhouette views of a full size racket model were used to demonstrate the potential of the method to estimate 3D racket position during a simplified serve scenario. From a camera distance of 14 m, 3D racket position was estimated providing a spatial accuracy of 1.9 ± 0.14 mm, similar to recent 3D video marker tracking studies of tennis.

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Section: Construction Of a Calibrated Set Of Silhouette Viewsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single view silhouette fitting techniques for estimating tennis racket position

et al. 2017

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“…It is really difficult to rate because it is only a visual observation. For this reason, every year, we have many new studies concerning the automatic and semi-automatic analyses of movement in sport [4,[12][13][14][15][16][17]. For example, Rahma et al [13] have developed a system to automatically analyse a basketball player's free throw using recorded video sequences.…”

Section: Introductionmentioning

confidence: 99%

“…This system showed the trajectory of the players' moves in both defence and offence. Another study proposed the motion tracking of a tennis racket using a monocular camera and the markerless technique [17], whereas the work by Sheets et al [19] makes use of a markerless motion capture system to test for kinematic differences in the lower back, shoulder, elbow, wrist, and racket between the flat, kick, and slice serves. In the study, seven male NCAA Division 1 players were tested on an outdoor court in daylight conditions.…”

Section: Introductionmentioning

confidence: 99%

A System for Analysing the Basketball Free Throw Trajectory Based on Particle Swarm Optimization

et al. 2018

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This paper describes a system for the automatic detection and tracking of a ball trajectory during a free throw. The tracking method is based on a particle swarm optimization (PSO) algorithm. The proposed method allows for the measurement of selected parameters of a basketball free throw trajectory. Ten parameters (four distances, three velocities, and three angle parameters) were taken into account. The research material included 200 sequences captured by a 100 Hz monocular camera. The study was based on a group of 30 basketball players who played in the Polish Second Division during the 2015/2016 season and the Youth Polish National Team in 2017. The experimental results showed the differences between the parameters in both missed and hit throws. The proposed system may be used in the training process as a tool to improve the technique of the free throw in basketball.

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“…The main goal of this paper was to evaluate the teamwork quantitatively based on movement of the players in game. Another study proposes motion tracking of a tennis racket using a monocular camera and markerless technique (Elliott et al, 2014). Whereas the work by (Sheets et al, 2011) makes use of a markerless motion capture system to test for kinematic differences at the lower back, shoulder, elbow, wrist, and racquet between the flat, kick, and slice serves.…”

Section: Introductionmentioning

confidence: 99%

Markerless Motion Tracking in Evaluation of Hurdle Clearance Parameters

Przednówek

Krzeszowski

Iskra³

et al. 2014

Proceedings of the 2nd International Congress on Sports Sciences Research and Technology Support

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In this study, implementation of markerless method of human body motion tracking as a tool of measurement of hurdle clearance kinematic parameters was presented. The analysis involved 5 hurdle runners at various training levels. Recording of video sequences was carried out under simulated starting conditions of a 110 m hurdle race. Kinematic parameters were determined based on the analysis of images recorded with a 100 Hz monocular camera. The suggested method does not involve using any special clothes, markers or estimation support techniques. In the study, the basic numerical characteristics of twenty estimated parameters were presented. The accuracy of determined hurdle clearance parameters was verified by comparison of estimated poses with the ground truth pose. As the quality criterion, the MAE (Mean Absolute Error) was adopted. In the distance parameters, the least error was obtained for the distance between the center of mass (CM) and the hurdle at the first hurdle clearance phase (MAE = 22.0 mm). For the angular parameters, the least error was obtained for the leg angle at the first hurdle clearance phase (MAE = 3.1 •). The level of computed errors showed that the presented method can be used for estimation of hurdle clearance kinematic parameters.

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Markerless Tracking of Tennis Racket Motion Using a Camera

Cited by 14 publications

References 11 publications

Single view silhouette fitting techniques for estimating tennis racket position

Single view silhouette fitting techniques for estimating tennis racket position

A System for Analysing the Basketball Free Throw Trajectory Based on Particle Swarm Optimization

Markerless Motion Tracking in Evaluation of Hurdle Clearance Parameters

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