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The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Recognizing Human Actions Using 3D Skeletal Information and CNNs

Authors : Antonios Papadakis, Eirini Mathe, Ioannis Vernikos, Apostolos Maniatis, Evaggelos Spyrou, Phivos Mylonas

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

In this paper we present an approach for the recognition of human actions targeting at activities of daily living (ADLs). Skeletal information is used to create images capturing the motion of joints in the 3D space. These images are then transformed to the spectral domain using 4 well-known image transforms. A deep Convolutional Neural Network is trained on those images. Our approach is thoroughly evaluated using a well-known, publicly available challenging dataset and for a set of actions that resembles to common ADLs, covering both cross-view and cross-subject cases.

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Footnotes
Literature
1.
Abadi, M., et al.: TensorFlow: a system for large-scale maching learning. In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2016)
2.
Berretti, S., Daoudi, M., Turaga, P., Basu, A.: Representation, analysis, and recognition of 3D humans: a survey. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 14(1S), 16 (2018)
3.
4.
Du, Y., Fu, Y., Wang, L.: Skeleton based action recognition with convolutional neural network. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 579–583. IEEE (2015)
5.
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE (2013)
6.
Hou, Y., Li, Z., Wang, P., Li, W.: Skeleton optical spectra-based action recognition using convolutional neural networks. IEEE Trans. Circuits Syst. Video Technol. 28(3), 807–811 (2018)CrossRef
7.
Jiang, W., Yin, Z.: Human activity recognition using wearable sensors by deep convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1307–1310 (2015)
8.
Ke, Q., An, S., Bennamoun, M., Sohel, F., Boussaid, F.: SkeletonNet: mining deep part features for 3-D action recognition. IEEE Signal Process. Lett. 24(6), 731–735 (2017)CrossRef
9.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
10.
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: 2011 International Conference on Computer Vision, pp. 2556–2563. IEEE (2011)
11.
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
12.
Lawton, M.P., Brody, E.M.: Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontol. 9(3 Part 1), 179–186 (1969)CrossRef
13.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
14.
Li, C., Hou, Y., Wang, P., Li, W.: Joint distance maps based action recognition with convolutional neural networks. IEEE Signal Process. Lett. 24(5), 624–628 (2017)CrossRef
15.
Liu, C., Hu, Y., Li, Y., Song, S., Liu, J.: PKU-MMD: a large scale benchmark for continuous multi-modal human action understanding. arXiv preprint arXiv:​1703.​07475 (2017)
16.
Liu, M., Liu, H., Chen, C.: Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognit. 68, 346–362 (2017)CrossRef
17.
Mathe, E., Mitsou, A., Spyrou, E., Mylonas, Ph.: Arm gesture recognition using a convolutional neural network. In: Proceedings of International Workshop Semantic and Social Media Adaptation and Personalization (SMAP) (2018)
18.
Mathe, E., Maniatis, A., Spyrou, E., Mylonas, Ph.: A deep learning approach for human action recognition using skeletal information. In: Proceedings of World Congress “Genetics, Geriatrics and Neurodegenerative Diseases Research” (GeNeDiS) (2018)
19.
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), vol. 03, pp. 32–36. IEEE Computer Society (2004)
20.
Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: NTU RGB+D: a large scale dataset for 3D human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010–1019 (2016)
21.
Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:​1212.​0402 (2012)
22.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH
23.
Wang, P., Li, W., Ogunbona, P., Wan, J., Escalera, S.: RGB-D-based human motion recognition with deep learning: a survey. Comput. Vis. Image Underst. 171, 118–139 (2018)CrossRef
24.
Wang, P., Li, W., Li, C., Hou, Y.: Action recognition based on joint trajectory maps with convolutional neural networks. Knowl.-Based Syst. 158, 43–53 (2018)CrossRef
Metadata
Title
Recognizing Human Actions Using 3D Skeletal Information and CNNs
Authors
Antonios Papadakis
Eirini Mathe
Ioannis Vernikos
Apostolos Maniatis
Evaggelos Spyrou
Phivos Mylonas
Copyright Year
2019
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-030-20256-9

Electronic ISBN: 978-3-030-20257-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-20257-6_44

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