Omnidirectional Wheelchair Vision with Small Reflect Mirrors for Tennis Ball Tracking

2017 ◽  
pp. 823-833
Author(s):  
Keita Matsuo ◽  
Leonard Barolli
Keyword(s):  
Tennis Ball ◽  
Ball Tracking

Tennis Ball Tracking Using a Two-Layered Data Association Approach

2015 ◽  
Vol 17 (2) ◽  
pp. 145-156 ◽  
Author(s):  
Xiangzeng Zhou ◽  
Lei Xie ◽  
Qiang Huang ◽  
Stephen J. Cox ◽  
Yanning Zhang
Keyword(s):  
Data Association ◽  
Tennis Ball ◽  
Ball Tracking

scholarly journals Characterisation of ball impact conditions in professional tennis: Matches played on hard court

2016 ◽  
Vol 230 (4) ◽  
pp. 236-245 ◽  
Author(s):  
Ben Lane ◽  
Paul Sherratt ◽  
Hu Xiao ◽  
Andy Harland
Keyword(s):  
Research And Development ◽  
Tennis Ball ◽  
Ball Impact ◽  
Total Data ◽  
Ball Tracking ◽  
Professional Tennis ◽  
Post Impact ◽  
Tennis Balls ◽  
The Impact

To assess ball performance for research and development purposes requires greater understanding of the impact conditions a tennis ball experiences in professional tournament play. Ball tracking information taken from three consecutive years of an ATP 250 tour event played on hard court was analysed. The frequency of first serves, second serves, racket impacts and surface impacts was assessed per game and extrapolated to show how many impacts a single ball is subjected to. Where applicable the pre- and post-impact velocities and angles were found, and the distribution of each was analysed. In total, data from 65 matches comprising 1505 games were analysed. On average, each game contained 70.26 (±16.23) impacts, of which 9.23%, 3.16%, 37.78% and 49.83% were first serves, second serves, racket impacts and surface impacts, respectively. As a result, assuming all balls in play are used evenly, a single ball is expected to be subjected to 105 (±24) impacts over the course of the nine games that it is in play. The results of the investigation could be used to design a wear protocol capable of artificially wearing tennis balls in a way that is representative of professional play.

Painless Tennis Ball Tracking System

2018 ◽  
Author(s):  
Wook-Sung Yoo ◽  
Zach Jones ◽  
Henok Atsbaha ◽  
David Wingfield
Keyword(s):  
Tracking System ◽  
Tennis Ball ◽  
Ball Tracking

scholarly journals A Cluster Analysis Approach to Profile Men and Women’s Volley Positions in Professional Tennis Matches (Doubles)

2021 ◽  
Vol 13 (11) ◽  
pp. 6370
Author(s):  
Rafael Martínez-Gallego ◽  
Jesús Ramón-Llin ◽  
Miguel Crespo
Keyword(s):  
Cluster Analysis ◽  
Performance Indicators ◽  
Tennis Ball ◽  
Match Play ◽  
Different Types ◽  
Ball Tracking ◽  
Professional Tennis ◽  
Inferential Analysis ◽  
The Impact ◽  
Tracking Technology

(1) Background: Tennis ball tracking technology allows the aquirement of novel and reliable data about several performance indicators, such as volley positions. This information is key to understand match dynamics in doubles tennis and to better help preparing players for the demands they will face in match play. As such, the purpose of this study was to describe and compare the different types of volley positions in men’s and women’s doubles professional tennis. (2) Methods: Ball tracking data were collected for 46 women (Billie Jean King Cup) and 96 men’s doubles matches (Davis Cup). The variables used were the distance to the net, the distance to the centre of the court and the height of the impact. A K-Means cluster analysis was used to identify in each subsample different profiles of volley locations. (3) Results: The inferential analysis revealed differences in men’s (distance to the net η2 = 0.72, distance to the centre of the court η2 = 0.77 and impact height η2 = 0.63) and women’s subsamples (distance to the net η2 = 0.48, distance to the centre of the court η2 = 0.52 and impact height η2 = 0.51). (4) Conclusions: The results allowed the suggestion of a higher variability in men’s matches, as there were seven different clusters identified, and only four in women’s.

scholarly journals T-RexNet—A Hardware-Aware Neural Network for Real-Time Detection of Small Moving Objects

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1252
Author(s):  
Alessio Canepa ◽  
Edoardo Ragusa ◽  
Rodolfo Zunino ◽  
Paolo Gastaldo
Keyword(s):  
Neural Network ◽  
Moving Objects ◽  
Tennis Ball ◽  
Single Shot ◽  
Aerial Surveillance ◽  
Convolutional Network ◽  
General Applicability ◽  
Ball Tracking ◽  
Original Picture ◽  
High Level

This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network includes two parallel paths: the first path processes both the original picture, in gray-scale format, and differences between consecutive frames; in the second path, differences between a set of three consecutive frames is only handled. As compared with generic object detectors, the method limits the depth of the convolutional network to make it less sensible to high-level features and easier to train on small objects. The simple, Hardware-efficient architecture attains its highest accuracy in the presence of videos with static framing. Deploying our architecture on the NVIDIA Jetson Nano edge-device shows its suitability to embedded systems. To prove the effectiveness and general applicability of the approach, real-world tests assessed the method performances in different scenarios, namely, aerial surveillance with the WPAFB 2009 dataset, civilian surveillance using the Chinese University of Hong Kong (CUHK) Square dataset, and fast tennis-ball tracking, involving a custom dataset. Experimental results prove that T-RexNet is a valid, general solution to detect small moving objects, which outperforms in this task generic existing object-detection approaches. The method also compares favourably with application-specific approaches in terms of the accuracy vs. speed trade-off.

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