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The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

RGB-D-based human motion recognition with deep learning: A survey

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Wang, Pichao, Li, Wanqing, Ogunbona, Philip, Wan, Jun, Escalera, Sergio·2018·Computer Vision and Image Understanding

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Citations

Cites

Show all 26 references

Research works printed but not linked: 39

  1. Aggarwal, 1999, Human motion analysis: a review
  2. Aggarwal, 2014, Human activity recognition from 3D data: a review, Pattern Recognit. Lett., 48, 70, 10.1016/j.patrec.2014.04.011 · DOI · Paper page
  3. Bobick, 2001, The recognition of human movement using temporal templates, IEEE Trans. Pattern Anal. Mach. Intell., 23, 257, 10.1109/34.910878 · DOI · Paper page
  4. Chen, 2013, A survey of human motion analysis using depth imagery, Pattern Recognit. Lett., 34, 1995, 10.1016/j.patrec.2013.02.006 · DOI · Paper page
  5. Cheng, 2016, Survey on 3D hand gesture recognition, IEEE Trans. Circuits Syst. Video Technol., 26, 1659, 10.1109/TCSVT.2015.2469551 · DOI · Paper page
  6. Du, 2016, Representation learning of temporal dynamics for skeleton-based action recognition, IEEE Trans. Image Process., 25, 3010, 10.1109/TIP.2016.2552404 · DOI
  7. Duan, 2016, A unified framework for multi-modal isolated gesture recognition, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 9, 39:1
  8. Escalera, 2016, Challenges in multimodal gesture recognition, J. Mach. Learn. Res., 17, 1
  9. Fernando, 2016, Rank pooling for action recognition, IEEE Trans. Pattern Anal. Mach. Intell., 39, 773, 10.1109/TPAMI.2016.2558148 · DOI · Paper page
  10. Guo, 2014, A survey on still image based human action recognition, Pattern Recognit., 47, 3343, 10.1016/j.patcog.2014.04.018 · DOI · Paper page
  11. Guyon, 2014, The Chalearn gesture dataset (CGD 2011), Mach. Vis. Appl., 25, 1929, 10.1007/s00138-014-0596-3 · DOI
  12. Han, 2017, Space-time representation of people based on 3D skeletal data: a review
  13. Herath, 2017, Going deeper into action recognition: a survey, Image Vis. Comput., 60, 4, 10.1016/j.imavis.2017.01.010 · DOI · Paper page
  14. Hoai, 2014, Max-margin early event detectors, Int. J. Comput. Vis., 107, 191, 10.1007/s11263-013-0683-3 · DOI · Paper page
  15. Ijjina, 2016, Classification of human actions using pose-based features and stacked auto encoder, Pattern Recognit. Lett., 83, 268, 10.1016/j.patrec.2016.03.021 · doi:10.1016/j.patrec.2016.03.021
  16. Ji, 2013, 3D Convolutional neural networks for human action recognition, IEEE Trans. Pattern Anal. Mach. Intell., 35, 221, 10.1109/TPAMI.2012.59 · DOI · Paper page
  17. Jiang, 2015, Multi-layered gesture recognition with kinect, J. Mach. Learn. Res., 16, 227
  18. Ke, 2017, Skeletonnet: mining deep part features for 3D action recognition, IEEE Signal Process Lett, 10.1109/LSP.2017.2690339 · DOI · Paper page
  19. Li, 2017, Joint distance maps based action recognition with convolutional neural networks, IEEE Signal Process Lett, 24, 624, 10.1109/LSP.2017.2678539 · DOI · Paper page
  20. Liu, 2017, Global context-aware attention LSTM networks for 3D action recognition, 10.1007/978-3-319-42999-1 · DOI · Paper page
  21. Liu, 2017, Enhanced skeleton visualization for view invariant human action recognition, Pattern Recognit., 68, 346, 10.1016/j.patcog.2017.02.030 · DOI · Paper page
  22. Liu, 2016, 3D-Based deep convolutional neural network for action recognition with depth sequences, Image Vis. Comput., 55, 93, 10.1016/j.imavis.2016.04.004 · DOI
  23. Luo, 2017, Unsupervised learning of long-term motion dynamics for videos, 10.1007/978-3-319-42999-1 · DOI · Paper page
  24. Presti, 2016, 3D skeleton-based human action classification: a survey, Pattern Recognit., 53, 130, 10.1016/j.patcog.2015.11.019 · DOI · Paper page
  25. Salakhutdinov, 2013, Learning with hierarchical-deep models, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1958, 10.1109/TPAMI.2012.269 · DOI · Paper page
  26. Shahroudy, 2017, Deep multimodal feature analysis for action recognition in RGB+ d videos, IEEE Trans. Pattern Anal. Mach. Intell., 40, 1045, 10.1109/TPAMI.2017.2691321 · DOI · Paper page
  27. Simonyan, K., Zisserman, A., 2014b. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. · arXiv
  28. Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting., J. Mach. Learn. Res., 15, 1929
  29. Turaga, 2008, Machine recognition of human activities: a survey, IEEE Trans. Circ. Syst. Video Technol., 18, 1473, 10.1109/TCSVT.2008.2005594 · DOI · Paper page
  30. Varol, 2018, Long-term temporal convolutions for action recognition, IEEE Trans. Pattern Anal. Mach. Intell., 1
  31. Wan, 2016, Explore efficient local features from RGB-D data for one-shot learning gesture recognition, IEEE Trans. Pattern Anal. Mach. Intell., 38, 1626, 10.1109/TPAMI.2015.2513479 · DOI
  32. Wan, 2013, One-shot learning gesture recognition from RGB-D data using bag of features, J. Mach. Learn. Res., 14, 2549
  33. Wang, 2003, Recent developments in human motion analysis, Pattern Recognit., 36, 585, 10.1016/S0031-3203(02)00100-0 · DOI · Paper page
  34. Wang, 2018, Depth pooling based large-scale 3d action recognition with convolutional neural networks, IEEE Trans. Multim., 20, 1051, 10.1109/TMM.2018.2818329 · DOI · Paper page
  35. Wang, 2016, Action recognition from depth maps using deep convolutional neural networks, THMS, 46, 498
  36. Wu, 2016, Deep dynamic neural networks for multimodal gesture segmentation and recognition, IEEE Trans. Pattern Anal. Mach. Intell., 38, 1583, 10.1109/TPAMI.2016.2537340 · DOI · Paper page
  37. Yeung, 2017, Every moment counts: dense detailed labeling of actions in complex videos, Int. J. Comput. Vis., 126, 375, 10.1007/s11263-017-1013-y · DOI · Paper page
  38. Zhang, 2016, RGB-D-based action recognition datasets: a survey, Pattern Recognit., 60, 86, 10.1016/j.patcog.2016.05.019 · DOI · Paper page
  39. Zhu, 2016, From handcrafted to learned representations for human action recognition: a survey, Image Vis. Comput., 55, 42, 10.1016/j.imavis.2016.06.007 · DOI

Cited by

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Metadata

Authors
Wang, P., Li, W., Ogunbona, P., Wan, J., & Escalera, S.
Venue
Computer Vision and Image Understanding
Publisher
Elsevier BV
Published
2018-06
Volume
171
Pages
118-139
ISSN
1077-3142 (ISSN-L)
OpenAlex
W2963177663
Cited by
402
References
26
FWCI
25.55
Type
journal article
Language
en
Open access
green
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • National Natural Science Foundation of China (61502491)
  • MINECO

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CoChat. (2026). Citation check: RGB-D-based human motion recognition with deep learning: A survey. https://app.cochat.ai/papers/10.1016/j.cviu.2018.04.007
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Wang, P., Li, W., Ogunbona, P., Wan, J., & Escalera, S. (2018). RGB-D-based human motion recognition with deep learning: A survey. Computer Vision and Image Understanding. https://doi.org/10.1016/j.cviu.2018.04.007

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