Have a personal or library account? Click to login
The application of topological data analysis to human motion recognition Cover

The application of topological data analysis to human motion recognition

Open Access
|Aug 2021

References

  1. Anirudh, R., Venkataraman, V., Natesan Ramamurthy, K., & Turaga, P. (2016). A riemannian framework for statistical analysis of topological persistence diagrams. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 68–76).10.1109/CVPRW.2016.132
  2. Bhaskar, D., Manhart, A., Milzman, J., Nardini, J. T., Storey, K. M., Topaz, C. M., & Ziegelmeier, L. (2019). Analyzing collective motion with machine learning and topology. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(12), 123–125.10.1063/1.5125493702742731893635
  3. Billon, R., Nédélec, A., & Tisseau, J. (2008). Gesture recognition in flow based on PCA and using multiagent system. In Proceedings of the 2008 ACM symposium on Virtual reality software and technology (pp. 239–240).10.1145/1450579.1450632
  4. Bottino, A., De Simone, M., & Laurentini, A. (2007). Recognizing human motion using eigensequences. Journal of WSCG, 15, 135–142.
  5. Choi, W., Li, L., Sekiguchi, H., & Hachimura, K. (2013). Recognition of gait motion by using data mining. In 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013) (pp. 1213–1216). IEEE.10.1109/ICCAS.2013.6704173
  6. Choi, W., Ono, T., & Hachimura, K. (2009). Body Motion Analysis for Similarity Retrieval of Motion Data and Its Evaluation. In 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (pp. 1177–1180). IEEE.10.1109/IIH-MSP.2009.174
  7. Choi, W., Sekiguchi, H., & Hachimura, K. (2009). Analysis of Gait Motion by Using Motion Capture in the Japanese Traditional Performing Arts. In 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (pp. 1164–1167). IEEE.10.1109/IIH-MSP.2009.171
  8. Cornacchia, M., Ozcan, K., Zheng, Y., & Velipasalar, S. (2016). A survey on activity detection and classification using wearable sensors. IEEE Sensors Journal, 17(2), 386–403.10.1109/JSEN.2016.2628346
  9. Das, S. R., Wilson, R. C., Lazarewicz, M. T., & Finkel, L. H. (2006). Two-stage PCA extracts spatiotemporal features for gait recognition. Journal of multimedia, 1(5), 9–17.
  10. Dirafzoon, A., Lokare, N., & Lobaton, E. (2016). Action classification from motion capture data using topological data analysis. In 2016 IEEE global conference on signal and information processing (globalSIP) (pp. 1260–1264). IEEE.10.1109/GlobalSIP.2016.7906043
  11. Edelsbrunner, H., & Harer, J. (2010). Computational topology: an introduction. American Mathematical Soc.
  12. Edelsbrunner, H., Letscher, D., & Zomorodian, A. (2000). Topological persistence and simplification. Discrete & Computational Geometry, 28(4), 511–533.10.1007/s00454-002-2885-2
  13. Funakoshi, G. (1996). Karate-Do Kyo-han; The Master Text. Kodansha America LLC.
  14. Ghrist, R. (2008). Barcodes: the persistent topology of data. Bulletin of the American Mathematical Society, 45(1), 61–75.10.1090/S0273-0979-07-01191-3
  15. Github (online). https://github.com/browarsoftware/MoCapEigen/tree/master/data [access: 17/12/2020].
  16. Hachaj, T. (2019). Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features. Symmetry, 11(10), 1264.10.3390/sym11101264
  17. Hachaj, T., & Ogiela, M. R. (2018). Classification of Karate Kicks with Hidden Markov Models Classifier and Angle-Based Features. In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1–5). IEEE.10.1109/CISP-BMEI.2018.8633251
  18. Hachaj, T., Piekarczyk, M., & Ogiela, M. R. (2017). Human actions analysis: Templates generation, matching and visualization applied to motion capture of highly-skilled karate athletes. Sensors, 17(11), 2590.10.3390/s17112590
  19. Idris, W. M. R. W., Rafi, A., Bidin, A., Jamal, A. A., & Fadzli, S. A. (2019). A systematic survey of martial art using motion capture technologies: the importance of extrinsic feedback. Multimedia Tools and Applications, 78(8), 10113–10140.10.1007/s11042-018-6624-y
  20. Kim, H. C., Kim, D., & Bang, S. Y. (2002). Face recognition using the mixture-ofeigenfaces method. Pattern Recognition Letters, 23(13), 1549–1558.10.1016/S0167-8655(02)00119-8
  21. Ko, J. H., Han, D. W., & Newell, K. M. (2018). Skill level changes the coordination and variability of standing posture and movement in a pistol-aiming task. Journal of Sports Sciences, 36(7), 809–816.10.1080/02640414.2017.134349028628398
  22. Lee, M., Roan, M., & Smith, B. (2009). An application of principal component analysis for lower body kinematics between loaded and unloaded walking. Journal of biomechanics, 42(14), 2226–2230.10.1016/j.jbiomech.2009.06.05219674748
  23. Mantovani, G., Ravaschio, A., Piaggi, P., & Landi, A. (2010). Fine classification of complex motion pattern in fencing. Procedia Engineering, 2(2), 3423–3428.10.1016/j.proeng.2010.04.168
  24. Mokari, M., Mohammadzade, H., Ghojogh, B. (2020). Recognizing involuntary actions from 3D skeleton data using body states. Scientia Iranica, 27(3), 1424–1436.
  25. Mrozek, M., Żelawski, M., Gryglewski, A., Han, S., & Krajniak, A. (2012). Homological methods for extraction and analysis of linear features in multidimensional images. Pattern Recognition, 45(1), 285–298.10.1016/j.patcog.2011.04.020
  26. Presti, L. L., & La Cascia, M. (2016). 3D skeleton-based human action classification: A survey. Pattern Recognition, 53, 130–147.10.1016/j.patcog.2015.11.019
  27. Som, A., Thopalli, K., Natesan Ramamurthy, K., Venkataraman, V., Shukla, A., & Turaga, P. (2018). Perturbation robust representations of topological persistence diagrams. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 617–635).10.1007/978-3-030-01234-2_38
  28. Świtoński, A., Mucha, R., Danowski, D., Mucha, M., Polański, A., Cieślar, G., & Sieroń, A. (2011). Diagnosis of the motion pathologies based on a reduced kinematical data of a gait. Przegląd Elektrotechniczny, 87(12), 173–176.
  29. Tralie, C. (2016). High-dimensional geometry of sliding window embeddings of periodic videos. In 32nd International Symposium on Computational Geometry (SoCG 2016). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
  30. Tralie, C. J., & Berger, M. (2018). Topological eulerian synthesis of slow motion periodic videos. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 3573–3577). IEEE.10.1109/ICIP.2018.8451014
  31. Umeda, Y. (2017). Time series classification via topological data analysis. Information and Media Technologies, 12, 228–239.10.1527/tjsai.D-G72
  32. Vejdemo-Johansson, M., Pokorny, F. T., Skraba, P., & Kragic, D. (2015). Cohomological learning of periodic motion. Applicable algebra in engineering, communication and computing, 26(1–2), 5–26.10.1007/s00200-015-0251-x
  33. Venkataraman, V., Ramamurthy, K. N., & Turaga, P. (2016). Persistent homology of attractors for action recognition. In 2016 IEEE international conference on image processing (ICIP) (pp. 4150–4154). IEEE.10.1109/ICIP.2016.7533141
  34. Zago, M., Pacifici, I., Lovecchio, N., Galli, M., Federolf, P. A., & Sforza, C. (2017). Multi-segmental movement patterns reflect juggling complexity and skill level. Human Movement Science, 54, 144–153.10.1016/j.humov.2017.04.01328499158
  35. Zomorodian, A., & Carlsson, G. (2005). Computing persistent homology. Discrete & Computational Geometry, 33(2), 249–274.10.1007/s00454-004-1146-y
DOI: https://doi.org/10.37705/TechTrans/e2021011 | Journal eISSN: 2353-737X | Journal ISSN: 0011-4561
Language: English
Submitted on: Dec 17, 2020
Accepted on: Jul 5, 2021
Published on: Aug 18, 2021
Published by: Cracow University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2021 Marcin Żelawski, Tomasz Hachaj, published by Cracow University of Technology
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 License.