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- Publisher Website: 10.1145/3297097.3297115
- Scopus: eid_2-s2.0-85061528559
- WOS: WOS:000470228200008
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Conference Paper: Artificial Intelligence for Sport Actions and Performance Analysis using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM)
Title | Artificial Intelligence for Sport Actions and Performance Analysis using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) |
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Authors | |
Keywords | Artificial Intelligence Deep Learning Human Activity Recognition LSTM RNN Sport Performance Analysis |
Issue Date | 2018 |
Publisher | Association for Computing Machinery. |
Citation | Proceedings of 2018 4th International Conference on Robotics and Artificial Intelligence (ICRAI 2018), Guangzhou, China, 17-19 November 2018, p. 40-44 How to Cite? |
Abstract | The development of Human Action Recognition (HAR) system is getting popular. This project developed a HAR system for the application in the surveillance system to minimize the man-power for providing security to the citizens such as public safety and crime prevention. In this research, deep learning network using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are used to analyze dynamic video motion of sport actions and classify different types of actions and their performance. It could classify different types of human motion with a small number of video frame for efficiency and memory saving. The current accuracy achieved is up to 92.9% but with high potential of further improvement. |
Persistent Identifier | http://hdl.handle.net/10722/278339 |
ISBN | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Fok, WWT | - |
dc.contributor.author | Chan, LCW | - |
dc.contributor.author | Chen, C | - |
dc.date.accessioned | 2019-10-04T08:12:05Z | - |
dc.date.available | 2019-10-04T08:12:05Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of 2018 4th International Conference on Robotics and Artificial Intelligence (ICRAI 2018), Guangzhou, China, 17-19 November 2018, p. 40-44 | - |
dc.identifier.isbn | 978-1-4503-6584-0 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278339 | - |
dc.description.abstract | The development of Human Action Recognition (HAR) system is getting popular. This project developed a HAR system for the application in the surveillance system to minimize the man-power for providing security to the citizens such as public safety and crime prevention. In this research, deep learning network using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are used to analyze dynamic video motion of sport actions and classify different types of actions and their performance. It could classify different types of human motion with a small number of video frame for efficiency and memory saving. The current accuracy achieved is up to 92.9% but with high potential of further improvement. | - |
dc.language | eng | - |
dc.publisher | Association for Computing Machinery. | - |
dc.relation.ispartof | 4th International Conference on Robotics and Artificial Intelligence (ICRAI 2018) | - |
dc.rights | 4th International Conference on Robotics and Artificial Intelligence (ICRAI 2018). Copyright © Association for Computing Machinery. | - |
dc.subject | Artificial Intelligence | - |
dc.subject | Deep Learning | - |
dc.subject | Human Activity Recognition | - |
dc.subject | LSTM | - |
dc.subject | RNN | - |
dc.subject | Sport Performance Analysis | - |
dc.title | Artificial Intelligence for Sport Actions and Performance Analysis using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Fok, WWT: wilton@hkucc.hku.hk | - |
dc.identifier.authority | Fok, WWT=rp00116 | - |
dc.identifier.doi | 10.1145/3297097.3297115 | - |
dc.identifier.scopus | eid_2-s2.0-85061528559 | - |
dc.identifier.hkuros | 306898 | - |
dc.identifier.spage | 40 | - |
dc.identifier.epage | 44 | - |
dc.identifier.isi | WOS:000470228200008 | - |
dc.publisher.place | New York, NY | - |