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Artificial Intelligence for Sport Actions and Performance Analysis using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM)

Published:17 November 2018Publication History

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.

References

  1. Timothy Revell. 2017. Computer vision algorithms pick out petty crime in CCTV footage. (January 2017). Retrieved July 9, 2018 from https://www.newscientist.com/article/2116970-computer-vision-algorithms-pick-out-petty-crime-in-cctv-footage/Google ScholarGoogle Scholar
  2. Paul Brown, Robin McGloughlin, and Chris Day. 2012. Poolview Plus ™ - Underwater Swimming Pool Camera, Drowing Prevention, Pool Safety. (September 2012). Retrieved June 22, 2018 from http://www.poolview.co.uk/poolview-plusGoogle ScholarGoogle Scholar
  3. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-term Memory. Neural Computation 9, 8 (December 1997), 1735--1780. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Zhe Cao and Tomas Simon. 2017. Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. (April 2017).Google ScholarGoogle Scholar
  5. Rohith Gandhi. 2018. Introduction to Sequence Models - RNN, Bidirectional RNN, LSTM, GRU. (June 2018). Retrieved November 10, 2018 from https://towardsdatascience.com/introduction-to-sequence-models-rnn-bidirectional-rnn-lstm-gru-73927ec9df15Google ScholarGoogle Scholar
  6. Andrej Karpathy. 2015. The Unreasonable Effectiveness of Recurrent Neural Networks. (May 2015). Retrieved September 23, 2018 from http://karpathy.github.io/2015/05/21/rnn-effectiveness/Google ScholarGoogle Scholar
  7. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2017. Deep learning, Cambridge, MA: The MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Amar Budhiraja. 2016. Learning Less to Learn Better-Dropout in (Deep) Machine learning. (December 2016). Retrieved September 13, 2018 from https://medium.com/@amarbudhiraja/https-medium-com-amarbudhiraja-learning-less-to-learn-better-dropout-in-deep-machine-learning-74334da4bfc5Google ScholarGoogle Scholar
  9. Amar Budhiraja. 2016. Learning Less to Learn Better-Dropout in (Deep) Machine learning. (December 2016). Retrieved September 13, 2018 from https://medium.com/@amarbudhiraja/https-medium-com-amarbudhiraja-learning-less-to-learn-better-dropout-in-deep-machine-learning-74334da4bfc5Google ScholarGoogle Scholar
  10. Srini Ananthakrishnan. 2018. Recognition Design. (October 2018). Retrieved September 23, 2018 from https://github.com/srianant/computer_vision/blob/master/openposeGoogle ScholarGoogle Scholar
  11. Pierre Baldi and Peter Sadowski. 2014. The dropout learning algorithm. Artificial Intelligence 210 (May 2014), 78--122.Google ScholarGoogle Scholar

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  1. Artificial Intelligence for Sport Actions and Performance Analysis using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM)

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    • Published in

      cover image ACM Other conferences
      ICRAI '18: Proceedings of the 4th International Conference on Robotics and Artificial Intelligence
      November 2018
      109 pages
      ISBN:9781450365840
      DOI:10.1145/3297097

      Copyright © 2018 ACM

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      Publication History

      • Published: 17 November 2018

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