Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis

Fernando, Tharindu; Denman, Simon; Sridharan, Sridha; Fookes, Clinton

doi:10.1109/tkde.2019.2911507

Cited by 21 publications

(19 citation statements)

References 37 publications

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“…The working principle of pinhole camera is as follows: light is emitted from a distance and projected into an image plane through the camera pinhole. If the focal length of the camera is f , the distance between the camera and the object is Z , any point of the object can be expressed as X , and the X corresponding point on the image plane is x ; then [ 9 ],

…”

Section: Construction Of the Machine Vision Algorithm Modelmentioning

confidence: 99%

Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

Liu

Sun

2021

Computational Intelligence and Neuroscience

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In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.

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…”

Section: Construction Of the Machine Vision Algorithm Modelmentioning

confidence: 99%

Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

Liu

Sun

2021

Computational Intelligence and Neuroscience

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“…We exploit the task-specific loss-function learning capability of the GAN framework to automatically learn a custom loss function [30]- [33] that facilitates these two tasks . The merit of this approach is that it allows us to learn a highly nonlinear loss, in contrast to a linear loss like cross entropy, to optimally capture the underlying semantics of the process.…”

Section: The Proposed Approachmentioning

confidence: 99%

Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection

Fernando

Sridharan

McLaren

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

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This paper presents a novel framework for Speech Activity Detection (SAD). Inspired by the recent success of multitask learning approaches in the speech processing domain, we propose a novel joint learning framework for SAD. We utilise generative adversarial networks to automatically learn a loss function for joint prediction of the frame-wise speech/ nonspeech classifications together with the next audio segment. In order to exploit the temporal relationships within the input signal, we propose a temporal discriminator which aims to ensure that the predicted signal is temporally consistent. We evaluate the proposed framework on multiple public benchmarks, including NIST OpenSAT' 17, AMI Meeting and HAVIC, where we demonstrate its capability to outperform state-of-the-art SAD approaches. Furthermore, our cross-database evaluations demonstrate the robustness of the proposed approach across different languages, accents, and acoustic environments.

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“…A few exceptions are several prediction models that have been proposed for relating shot and player features to a shot's outcome [Wei et al, 2013b[Wei et al, , 2016a or bounce location [Wei et al, 2013a[Wei et al, , 2016b. A recent study used a generative adversarial network to generate descriptions of a shot and player positions in the form of 2D flattened images [Fernando et al, 2019].…”

Section: Introductionmentioning

confidence: 99%

“…Another work assigned shot value during a point based on a Markovian model whose state transitions are based on coarsened locations of the ball and players at one time point [Floyd et al, 2019]. And a recent study used a generative adversarial network to generate descriptions of a shot and player positions in the form of 2D flattened images [Fernando et al, 2019]. These recent developments have been important in producing more advanced metrics of tennis performance.…”

Section: Introductionmentioning

confidence: 99%

Space-Time VON CRAMM: Evaluating Decision-Making in Tennis with Variational generatiON of Complete Resolution Arcs via Mixture Modeling

Kovalchik,

Ingram,

Weeratunga

et al. 2020

Preprint

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Sports tracking data are the high-resolution spatiotemporal observations of a competitive event. The growing collection of these data in professional sport allows us to address a fundamental problem of modern sport: how to attribute value to individual actions? Taking advantage of the smoothness of ball and player movement in tennis, we present a functional data framework for estimating expected shot value (ESV) in continuous time. Our approach is a three-step recipe: 1) a generative model for a full-resolution functional representation of ball and player trajectories using an infinite Bayesian Gaussian mixture model (GMM), 2) conditioning of the GMM on observed positional data, and 3) the prediction of shot outcomes given the functional encoding of a shot event. From the ESV we derive three metrics of central interest: value added with shot taking (VAST), Shot IQ, and value added with court coverage (VACC), which respectively attribute value to shot execution, shot selection and movement around the court. We rate player performance at the 2019 US Open on these advanced metrics and show how each adds a novel perspective to performance evaluation in tennis that goes beyond simple counts of outcomes by quantitatively assessing the decisions players make throughout a point.

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Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis

Cited by 21 publications

References 37 publications

Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection

Space-Time VON CRAMM: Evaluating Decision-Making in Tennis with Variational generatiON of Complete Resolution Arcs via Mixture Modeling

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