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Deep Learning for Domain-Specific Action Recognition in Tennis

Silvia Vinyes Mora, William J. Knottenbelt
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</a> </i> &nbsp;
For this reason we focus on the fine-grained action recognition in tennis and explore the capabilities of deep neural networks for this task.  ...  In order for action recognition to be useful in sports analytics a finer-grained action classification is needed.  ...  Some research has been done in the area of tennis action recognition.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvprw.2017.27">doi:10.1109/cvprw.2017.27</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/MoraK17.html">dblp:conf/cvpr/MoraK17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n67b5hpxgzdgrehq7ffjjwpiam">fatcat:n67b5hpxgzdgrehq7ffjjwpiam</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200319011949/http://openaccess.thecvf.com/content_cvpr_2017_workshops/w2/papers/Mora_Deep_Learning_for_CVPR_2017_paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/06/e8/06e872e1de44e5bd4ec546401ff4f06d6826e032.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvprw.2017.27"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory [article]

Jiaxin Cai, Xin Tang
<span title="2018-09-25">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recently, deep learning based methods have achieved promising performance for action recognition.  ...  In this paper, we propose weighted Long Short-Term Memory adopted with convolutional neural network representations for three dimensional tennis shots recognition.  ...  [2] have proposed a deep learning model for domain-specific tennis action recognition using RGB video content.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1808.00845v2">arXiv:1808.00845v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7qvnb7oot5bhfoenjczk4qi2de">fatcat:7qvnb7oot5bhfoenjczk4qi2de</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200906043747/https://arxiv.org/pdf/1808.00845v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/78/57/7857ac165329843f0c215daf935b042f79012ebe.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1808.00845v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications [article]

Fei Wu, Qingzhong Wang, Jian Bian, Haoyi Xiong, Ning Ding, Feixiang Lu, Jun Cheng, Dejing Dou
<span title="2022-06-02">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we present a survey on video action recognition for sports analytics.  ...  Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video action recognition to a higher level.  ...  Sports Dataset Model Year Performance ACASVA [189] HOG3D+CNN [216] 2020 93.78 Tennis THETIS TABLE VI DEEP VI LEARNING MODEL FOR GROUP ACTIVITY RECOGNITION IN SPORTS.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2206.01038v1">arXiv:2206.01038v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eqctk6342zdcrdbqggixmprp4e">fatcat:eqctk6342zdcrdbqggixmprp4e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220703155933/https://arxiv.org/pdf/2206.01038v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b3/d0/b3d0e66c56fd748d8dbd03366a44c0fda473667e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2206.01038v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Recognition Technology of Athlete's Limb Movement Combined Based on the Integrated Learning Algorithm

Fei Tan, Xiaoqing Xie, Guolong Shi
<span title="2021-09-06">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zlknqk4ahbcsthbafxw55emm7a" style="color: black;">Journal of Sensors</a> </i> &nbsp;
With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual  ...  Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis.  ...  Therefore, this paper applies the sensor-based human action recognition method to daily table tennis sports and designs recognition models for classifying four types of actions in table tennis: forehand  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/3057557">doi:10.1155/2021/3057557</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vwkhunppxvbkjc5ydyxlbxtj6u">fatcat:vwkhunppxvbkjc5ydyxlbxtj6u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210908015843/https://downloads.hindawi.com/journals/js/2021/3057557.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d6/58/d658a36566300d08efac42a60638106f15626ce2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/3057557"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

A Survey of Content-Aware Video Analysis for Sports

Huang-Chia Shih
<span title="">2018</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jqw2pm7kwvhchpdxpcm5ryoic4" style="color: black;">IEEE transactions on circuits and systems for video technology (Print)</a> </i> &nbsp;
Specifically, we focus on the video content analysis techniques applied in sportscasts over the past decade from the perspectives of fundamentals and general review, a content hierarchical model, and trends  ...  Finally, the paper summarizes the future trends and challenges for sports video analysis.  ...  Nevertheless, there is substantial room for development in sports video analysis with deep learning. Different sports have domain-specific semantic concepts, structures, and features.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcsvt.2017.2655624">doi:10.1109/tcsvt.2017.2655624</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rwqzu46sgfb7tpkcav4ysmh6ae">fatcat:rwqzu46sgfb7tpkcav4ysmh6ae</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823004342/https://arxiv.org/ftp/arxiv/papers/1703/1703.01170.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5c/4c/5c4ce36063dd3496a5926afd301e562899ff53ea.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcsvt.2017.2655624"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

TennisVid2Text: Fine-grained Descriptions for Domain Specific Videos [article]

Mohak Sukhwani, C.V. Jawahar
<span title="2015-11-26">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we attempt to describe videos from a specific domain - broadcast videos of lawn tennis matches.  ...  This demands a detailed low-level analysis of the video content, specially the actions and interactions among subjects. We address this by limiting our domain to the game of lawn tennis.  ...  This becomes challenging in a domain independent scenario due to almost innumerable possibilities. We make an attempt towards this goal by focusing on a domain specific setting -lawn tennis videos.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1511.08522v1">arXiv:1511.08522v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f6pwfzfbffhelml6ewwt5frw2q">fatcat:f6pwfzfbffhelml6ewwt5frw2q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200907200923/https://arxiv.org/pdf/1511.08522v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/a0/85a04f28c1696e33ae9a205dbd7531c97916eb18.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1511.08522v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Racket Motion Recognition Method Based on Improved Two-Stream Convolution Network

Zhao Hui-Qun, Computer School, North China University of Technology, Beijing 100144 China, Ye Wei
<span title="">2020</span> <i title="EJournal Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2uckwik5xjerdg36acn26gjq6e" style="color: black;">International Journal of Machine Learning and Computing</a> </i> &nbsp;
In this paper, a two-stream deep convolution network model for racket motion recognition and a set of algorithms for racket motion recognition are proposed.  ...  Based on the UCF101 standard data set, both the table tennis and badminton two action video were tested, and the accuracy of 66.92 was obtained.  ...  A recognition rate of 66.92% was obtained on the table tennis and tennis swing data sets in UCF101. Currently, deep convolution networks have been extensively studied in the field of computer vision.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18178/ijmlc.2020.10.4.972">doi:10.18178/ijmlc.2020.10.4.972</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/znidq5r43rdpllfihsozzn7lkm">fatcat:znidq5r43rdpllfihsozzn7lkm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201213033404/http://www.ijmlc.org/vol10/972-CT020.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/40/14/4014b5ff22db6a831ce3d553a7c04a1be05ad175.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18178/ijmlc.2020.10.4.972"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Deep learning in sport video analysis: a review

Keerthana Rangasamy, Muhammad Amir As'ari, Nur Azmina Rahmad, Nurul Fathiah Ghazali, Saharudin Ismail
<span title="2020-08-01">2020</span> <i title="Universitas Ahmad Dahlan"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/avuzjspx3nh5lboz3nsmpd3ba4" style="color: black;">TELKOMNIKA (Telecommunication Computing Electronics and Control)</a> </i> &nbsp;
The main purpose of this review paper is to compare and update review between traditional handcrafted approach and deep learning approach in sport video analysis based on human activity recognition, overview  ...  of recent study in video based human activity recognition in sport analysis and finally concluded with future potential direction in sport video analysis.  ...  [41] have proposed a domain-specific deep learning action recognition method by utilising pre-trained CNN with three-layered LSTM model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12928/telkomnika.v18i4.14730">doi:10.12928/telkomnika.v18i4.14730</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3iysvowdefdtlgzelj5t4w6uye">fatcat:3iysvowdefdtlgzelj5t4w6uye</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200613033938/http://journal.uad.ac.id/index.php/TELKOMNIKA/article/download/14730/8314" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3b/db/3bdbdcb85dc2ceac39b4bfa11c66345771b14399.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12928/telkomnika.v18i4.14730"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Does decision making transfer across similar and dissimilar sports?

André Roca, A. Mark Williams
<span title="">2017</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/g4d3yez3nfhozmfiqcib7y46u4" style="color: black;">Psychology of Sport And Exercise</a> </i> &nbsp;
Participants were required to decide on an appropriate 9 action to execute for each situation presented. 10 Results: Response accuracy was higher in the soccer decision-making task compared to 11 the basketball  ...  elements, supporting the importance both of specificity and 15 generality in expert performance. 16 17  ...  Acknowledgment 17 The authors would like to thank Aaron Giardelli for his assistance with the 18 production of the video-based test stimuli and data collection. 19  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.psychsport.2017.04.004">doi:10.1016/j.psychsport.2017.04.004</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6d34ppg3encffg5kruq7okwlei">fatcat:6d34ppg3encffg5kruq7okwlei</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430165738/https://research.stmarys.ac.uk/1477/1/Paper_DM%20Transfer%20across%20Similar%20and%20Dissimilar%20Sports_Accepted%20Version.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/76/31/76312d028c01f90e71751399ec3e6df7ffb40305.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.psychsport.2017.04.004"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data

Pu Yanan, Yan Jilong, Zhang Heng
<span title="2021-10-08">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations.  ...  Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.  ...  The machine learning method based on the time domain and frequency domain has a specific classification effect, but it is not ideal. ii.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21196685">doi:10.3390/s21196685</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34641004">pmid:34641004</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8513010/">pmcid:PMC8513010</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vdoutwxosrge7csc7jabp7ptsq">fatcat:vdoutwxosrge7csc7jabp7ptsq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220501091822/https://mdpi-res.com/d_attachment/sensors/sensors-21-06685/article_deploy/sensors-21-06685-v2.pdf?version=1633941689" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/53/b7/53b7b5752391d7dbb64d629f704e223de812f8f7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21196685"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513010" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Fusion of Video and Inertial Sensing for Deep Learning–Based Human Action Recognition

Haoran Wei, Roozbeh Jafari, Nasser Kehtarnavaz
<span title="2019-08-24">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Experiments are conducted using the publicly available dataset UTD-MHAD in which simultaneous video images and inertial signals are captured for a total of 27 actions.  ...  to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually.  ...  The second author participated in the discussions and provided partial internal funding for this work. Funding: This research received no external funding.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19173680">doi:10.3390/s19173680</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31450609">pmid:31450609</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6749419/">pmcid:PMC6749419</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/taf56uocdbavzntoy754gyrnc4">fatcat:taf56uocdbavzntoy754gyrnc4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200212150349/https://pdfs.semanticscholar.org/5d3c/10ec407c46887620c787fe2b15003456811d.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5d/3c/5d3c10ec407c46887620c787fe2b15003456811d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19173680"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749419" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Emily E Cust, Alice J Sweeting, Kevin Ball, Sam Robertson
<span title="2018-10-11">2018</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5khbyypd4rbdhdyjctsqr7knta" style="color: black;">Journal of Sports Sciences</a> </i> &nbsp;
The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data  ...  Included studies must have investigated a sportspecific movement and analysed via machine or deep learning methods for model development.  ...  Specifically, consistency between a 488 swim stroke detection model for continuous videos in swimming which was then applied to tennis 489 strokes with no domain-specific settings introduced (Victor et  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/02640414.2018.1521769">doi:10.1080/02640414.2018.1521769</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30307362">pmid:30307362</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h3flbavuivbilbo6ekwvmnjg6i">fatcat:h3flbavuivbilbo6ekwvmnjg6i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190713112620/http://vuir.vu.edu.au:80/37960/1/RJSP-2018-0332_R2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d0/2c/d02ce9e37a3fd647d2ac1c7e5e1580240260e05d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/02640414.2018.1521769"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> tandfonline.com </button> </a>

Convolutional neural networks for the analysis of broadcasted tennis games

Grigorios Tsagkatakis, Mustafa Jaber, Panagiotis Tsakalides
<span title="2018-01-28">2018</span> <i title="Society for Imaging Science &amp; Technology"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sajjfb6z3be2xbfjs4d2afwzyi" style="color: black;">IS&amp;T International Symposium on Electronic Imaging Science and Technology</a> </i> &nbsp;
In this is work we propose a architecture for the automated detection of scored points during tennis matches.  ...  Innovative deep learning architectures like Convolutional Neural Networks (CNNs), however are demonstrating remarkable performance in challenging image and video understanding tasks.  ...  Convolutional Neural Networks (CNNs) [2] , a particular deep learning architecture, have shown great promise in static image analysis task like object recognition and classification [3, 4] while more  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2352/issn.2470-1173.2018.2.vipc-206">doi:10.2352/issn.2470-1173.2018.2.vipc-206</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gvulxsb3djdi7ltp7fvbztecrm">fatcat:gvulxsb3djdi7ltp7fvbztecrm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190223144548/http://pdfs.semanticscholar.org/4883/75ae857a424febed7c0347cc9590989f01f7.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/48/83/488375ae857a424febed7c0347cc9590989f01f7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2352/issn.2470-1173.2018.2.vipc-206"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Deep learning—Accelerating Next Generation Performance Analysis Systems?

Heike Brock
<span title="2018-02-23">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tmebwgnfrvem5c5r53x7zxx3yq" style="color: black;">Proceedings (MDPI)</a> </i> &nbsp;
However, to date deep learning is only seldom applied to activity recognition problems of the human motion domain.  ...  Deep neural network architectures show superior performance in recognition and prediction tasks of the image, speech and natural language domains.  ...  Acknowledgments: No additional funding has been received in support of this research. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/proceedings2060303">doi:10.3390/proceedings2060303</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oyu77gvw2zadpfft5v7q5fzumq">fatcat:oyu77gvw2zadpfft5v7q5fzumq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190503050147/https://res.mdpi.com/proceedings/proceedings-02-00303/article_deploy/proceedings-02-00303-v2.pdf?filename=&amp;attachment=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f7/e3/f7e3a2b98f1746db563ac5a8870e170f9aab0e60.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/proceedings2060303"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition [article]

Yang Liu, Zhaoyang Lu, Jing Li, Tao Yang, Chao Yao
<span title="2019-11-25">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Existing deep learning methods for action recognition in videos require a large number of labeled videos for training, which is labor-intensive and time-consuming.  ...  Finally, the concatenation of the learned semantic feature representations from these three autoencoders are used to train the classifier for action recognition in videos.  ...  Some recent works use deep neural networks (particularly CNNs) to jointly learn the feature extractors and classifiers for action recognition in videos.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.10751v1">arXiv:1911.10751v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bp5i5wbb55bkpev5o6kqtbd2fy">fatcat:bp5i5wbb55bkpev5o6kqtbd2fy</a> </span>
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