| Title | Artificial Intelligence for Sport Actions and Performance Analysis Using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Fok, Wilton W. T., Chan, Louis C. W., Chen, Carol |
| Conference Name | Proceedings of the 2018 4th International Conference on Robotics and Artificial Intelligence |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6584-0 |
| Keywords | artificial intelligence, Deep Learning, deep video, human activity recognition, LSTM, Metrics, pubcrawl, Resiliency, RNN, Scalability, Sport Performance Analysis |
| 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. |
| URL | http://doi.acm.org/10.1145/3297097.3297115 |
| DOI | 10.1145/3297097.3297115 |
| Citation Key | fok_artificial_2018 |
Artificial Intelligence for Sport Actions and Performance Analysis Using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM)