A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

MonoTrack: Shuttle trajectory reconstruction from monocular badminton video

Trajectory estimation is a fundamental component of racket sport analytics, as the trajectory contains information not only about the winning and losing of each point, but also how it was won or lost. In sports such as badminton, players benefit from knowing the full 3D trajectory, as the height of shuttlecock or ball provides valuable tactical information. Unfortunately, 3D reconstruction is a notoriously hard problem, and standard trajectory estimators can only track 2D pixel coordinates. In this work, we present the first complete end-to-end system for the extraction and segmentation of 3D shuttle trajectories from monocular badminton videos. Our system integrates badminton domain knowledge such as court dimension, shot placement, physical laws of motion, along with vision-based features such as player poses and shuttle tracking. We find that significant engineering efforts and model improvements are needed to make the overall system robust, and as a by-product of our work, improve state-of-the-art results on court recognition, 2D trajectory estimation, and hit recognition.Comment: To appear in CVSports@CVPR 202

An effective trajectory-based algorithm for ball detection and tracking with application to the analysis of broadcast sports video

Ph.DDOCTOR OF PHILOSOPH

A time-motion analysis of elite women's hockey - implications for fitness assessment and training

To-date no large scale studies have been published that have used player tracking technology to investigate continuous time-motion analysis in the modern era of Women’s field hockey during Elite level International\ud competition to investigate positional differences and inform fitness training and testing. A new computerised time-motion analysis method, Trak Performance was used to analyse individual player movement (n = 54) from\ud 18 International Women’s hockey matches (18 defenders, 18 midfielders, 18 forwards). Overall analysis identified distance covered 9.1 ± 1.6 km, of which 74.7 ± 9.0% was covered in low intensity activity of stationary, walking and\ud jogging, 3.9 ± 2.4% match time was spent stationary. Mean sprint distance of 12.7 ± 1.7 m, with an average of 26.7 ± 11.5 s between each sprint. Positional differences were identified for the mean percentage of time spent, distances\ud covered in locomotion activity, the mean duration of rest between sprint bouts, the frequency of sprints and work to rest ratios. The majority of contrasts in movement characteristics occur between the defensive players and other outfield positions. Analysis of repeated-sprint ability revealed forwards undertake a significantly greater amount of 16 ± 9. Modern hockey dispels traditional positional roles with tactics and the more fluid nature of attacking plays requiring a more versatile player. Fitness assessment/training should therefore resemble the intermittent nature of the game with sprint recovery\ud periods reflecting the different positional demands

Multicamera System for Automatic Positioning of Objects in Game Sports

Garantir um sistema com múltiplas câmaras que seja capaz de extrair dados 3D da posição de uma bola durante um evento desportivo, através da análise e teste de técnicas de visão computacional (calibração de câmaras e reconstrução 3D)