Sign in Register Submit Manuscript

Damray Home

Location: Home >> Human Boxing Motion Prediction Using Neural Networks

  • donwnload article

    Download Article

  • donwnload article

    Share Article

Article

Human Boxing Motion Prediction Using Neural Networks

Jifei Liang

Intellectual Property Institute, Guangzhou 510632, China.

*Corresponding author: Jifei Liang

Published: November 29,2022 How to cite this paper

Abstract

Numerous thoughts that were previously deemed inconceivable have become a reality as a result of decades of technical progress and improvement. While flying automobiles are still in the far future, artificial intelligence that can predict your next move is rapidly approaching. Human motion prediction is a relatively new area of active research that is interesting for it’s potential of improving robot’s and other machinery’s ability to work with human, such as passing objects to human, and avoiding crash into human, etc. This thesis focuses on predicting human boxing moves based on RGB visual input as an artificially intelligent boxing trainer with the help of recurrent neural networks (RNNs). I study and compares the performance of six distinct neural network architectures. I have method 1, which includes four model architectures taking 3D joint data as input, and method 2, which includes two architectures that take RGB image as input. Based on the results of all my research, I have discovered the most effective and efficient architecture for scenarios with sparse data based on the outcome of my study.

KEYWORDS: Boxing, Motion prediction, Neural Network

References

[1] Yarrow Bouchard. Tesla’s deep learning at scale: Using billions of miles to train neural networks. https://towardsdatascience.com/ teslas-deep-learning-at-scale-7eed85b235d3, Dec 2019.

[2] Javier Dehesa, Andrew Vidler, Christof Lutteroth et al. Touch´e: Data-driven interactive sword fighting in virtual reality. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pages 1-14, 2020.

[3] Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller et al. Real-time multiperson 3d human pose estimation with a single rgb camera. arXiv preprint arXiv:1907.00837, 2019.

[4] Julieta Martinez, Michael J Black and Javier Romero. On human motion prediction using recurrent neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2891–2900, 2017.

[5] MMACHANNEL. What is the average punching speed? punching speed analyzed. Guinness World Records, 2021.

[6] Sepp Hochreiter and J¨urgenSchmidhuber. Long short-term memory. Neural computation, 9:1735–80, 12 1997. 

[7] Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee et al. Youtube-8m: A large-scale video classification benchmark. In ar-Xiv:1609.08675, 2016.

[8] Mart´ın Abadi, Ashish Agarwal, Paul Barham et al. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.

[9] NVIDIA, P´eterVingelmann, and Frank H.P. Fitzek. Cuda, release: 10.2.89, 2020.

[10] Sharan Chetlur, CliffWoolley, Philippe Vandermersch et al. cudnn: Efficient primitives for deep learning. CoRR, abs/1410.0759, 2014.

How to cite this paper

Jifei Liang. Human Boxing Motion Prediction Using Neural Networks. OA Journal of Computer Networking, 2022, 1(2), 42-48.


Copyright © 2022 Damray Co., Ltd. Privacy Policy | Terms and Conditions