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Comparison of Machine Learning Algorithms to Recognize Human Activities from Images and Videos Using Pose Estimation and Feature Extraction

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Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1 (FTC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1288))

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Abstract

In the last few years, there have been a lot of attempts to solve human activity recognition. Few of them are trying to solve the problem by using video streams or images. In machine learning algorithms, the conventional approach for classifying images or frames is to compare them as pixel by pixel which often leads to wrong assumptions. To solve this problem, pose estimation and visual feature extraction plays a vital role. In this research, these two techniques are applied to six different machine learning algorithms for image classification, and furthermore, a comparative analysis is being conducted among them.

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References

  1. Amato, G., Falchi, F.: KNN based image classification relying on local feature similarity. In: Proceedings of the 3rd International Conference on SImilarity Search and Applications, SISAP 2010, pp. 101–108. ACM Press, New York, USA (2010). https://doi.org/10.1145/1862344.1862360, http://portal.acm.org/citation.cfm?doid=1862344.1862360

  2. Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: new benchmark and state of the art analysis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)

    Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  4. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  5. Li, H., Xu, Z., Taylor, G., Studer, C., Goldstein, T.: Visualizing the loss landscape of neural nets. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, pp. 6389–6399. Curran Associates, Inc. (2018), http://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets.pdf

  6. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(85), 2825–2830 (2011). http://jmlr.org/papers/v12/pedregosa11a.html

  7. Rahit, K.M.H., Nabil, R.H., Huq, M.H.: Machine translation from natural language to code using long-short term memory. In: Advances in Intelligent Systems and Computing, vol. 1069, pp. 56–63. Springer (2020). https://doi.org/10.1007/978-3-030-32520-6_6

  8. Wang, H., An, W.P., Wang, X., Fang, L., Yuan, J.: Magnify-net for multi-person 2D pose estimation. In: Proceedings of the IEEE International Conference on Multimedia and Expo, July 2018. IEEE Computer Society (2018). https://doi.org/10.1109/ICME.2018.8486591

  9. Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(9), 207–244 (2009). http://jmlr.org/papers/v10/weinberger09a.html

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Acknowledgment

I would like to express my utmost gratitude towards K. M. Tahsin Hassan Rahit for his never-ending inspiration and encouragement in all of my (Hasibul Huq) research works. I will always appreciate for sharing the experience of my first publication [7] with you. Also, thanks to Future Technology Conference - 2020 committee for partially aiding us to join the conference. Special thanks to all of our family, friends, and well-wishers. Lastly, we would like to convey our special gratitude to all the workers who are fighting against the COVID-19 situation.

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Correspondence to Md Hasibul Huq .

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Huq, M.H., Alnakli, M., Jafrin, Z., Jenia, T.N. (2021). Comparison of Machine Learning Algorithms to Recognize Human Activities from Images and Videos Using Pose Estimation and Feature Extraction. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1. FTC 2020. Advances in Intelligent Systems and Computing, vol 1288. Springer, Cham. https://doi.org/10.1007/978-3-030-63128-4_7

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