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Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks Cover

Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Open Access
|Jun 2020

References

  1. [1] P. Premaratne, “Historical development of hand gesture recognition”, in Human Computer Interaction Using Hand Gestures. Cognitive Science and Technology. Singapore: Springer, 2014, pp. 5–29. https://doi.org/10.1007/978-981-4585-69-9_210.1007/978-981-4585-69-9_2
  2. [2] C. S. Chua, H. Guan, Y. K. Ho, “Model-based 3D hand posture estimation from a single 2D image”, Image and Vision computing, vol. 20, no. 3, 2002, pp. 191–202. https://doi.org/10.1016/S0262-8856(01)00094-410.1016/S0262-8856(01)00094-4
  3. [3] Z. Lai, Z. Yao, C. Wang, H. Liang, H. Chen, W. Xia, “Fingertips detection and hand gesture recognition based on discrete curve evolution with a kinect sensor”, 2016 Visual Communications and Image Processing (VCIP), IEEE, pp. 1–4, 2016. https://doi.org/10.1109/VCIP.2016.780546410.1109/VCIP.2016.7805464
  4. [4] C. Wang, Z. Liu, M. Zhu, J. Zhao, S. C. Chan, “A hand gesture recognition system based on canonical superpixel-graph”, Signal Processing: Image Communication, vol. 58, pp. 87–98, 2017. https://doi.org/10.1016/j.image.2017.06.01510.1016/j.image.2017.06.015
  5. [5] A. Joshi, C. Monnier, M. Betke, S. Sclaro, “A random forest approach to segmenting and classifying gestures”, 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), IEEE. pp. 1–7, 2015. https://doi.org/10.1109/FG.2015.716312610.1109/FG.2015.7163126
  6. [6] A. Ghotkar, P. Vidap, K. Deo, “Dynamic hand gesture recognition using hidden Markov Model by Microsoft Kinect Sensor”, International Journal of Computer Applications, vol. 150, no. 5, pp. 5–9, 2016. https://doi.org/10.5120/ijca201691149810.5120/ijca2016911498
  7. [7] H. D. Yang, “Sign language recognition with the kinect sensor based on conditional random fields”, Sensors, vol. 15, no. 1, pp. 135–147, 2015. https://doi.org/10.3390/s15010013510.3390/s150100135
  8. [8] A. Joshi, S. Ghosh, M. Betke, S. Sclaro, H. Pfister, “Personalizing gesture recognition using hierarchical bayesian neural networks”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 6513–6522. https://doi.org/10.1109/CVPR.2017.5610.1109/CVPR.2017.56
  9. [9] F. J. Ordó˜nez, D. Roggen, “Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition”, Sensors, vol. 16, no. 1, p. 115, 2016. https://doi.org/10.3390/s1601011510.3390/s16010115
  10. [10] P. Molchanov, S. Gupta, K. Kim, J. Kautz, “Hand gesture recognition with 3D convolutional neural networks”, in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition workshops, 2015, pp. 1–7. https://doi.org/10.1109/CVPRW.2015.730134210.1109/CVPRW.2015.7301342
  11. [11] N. C. Camgoz, S. Hadfield, O. Koller, R. Bowden, “Using convolutional 3D neural networks for user-independent continuous gesture recognition”, in 23rd International Conference on Pattern Recognition (ICPR), IEEE, 2016, pp. 49–54. https://doi.org/10.1109/ICPR.2016.789960610.1109/ICPR.2016.7899606
  12. [12] V. John, A. Boyali, S. Mita, M. Imanishi, N. Sanma, “Deep learning based fast hand gesture recognition using representative frames”, in International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2016, IEEE, 2016, pp. 1–8. https://doi.org/10.1109/DICTA.2016.779703010.1109/DICTA.2016.7797030
  13. [13] K. Lai, S. N. Yanushkevich, “CNN+RNN depth and skeleton based dynamic hand gesture recognition”, in 24th International Conference on Pattern Recognition (ICPR), IEEE, 2018, pp. 3451–3456. https://doi.org/10.1109/ICPR.2018.854571810.1109/ICPR.2018.8545718
  14. [14] M. Ma, Z. Gao, J. Wu, Y. Chen, Q. Zhu, “A recognition method of hand gesture based on stacked denoising autoencoder”, Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications, Advances in Intelligent Systems and Computing, Springer, Cham, vol. 891, 2018, pp. 736–744. https://doi.org/10.1007/978-3-030-03766-6_8310.1007/978-3-030-03766-6_83
  15. [15] K. Schindler, L. Van Gool, “Action snippets: How many frames does human action recognition require?”, in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, IEEE, 2008, pp. 1–8. https://doi.org/10.1109/CVPR.2008.458773010.1109/CVPR.2008.4587730
  16. [16] E. Ohn-Bar, M. M. Trivedi, “Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations”, IEEE Transactions on Intelligent Transportation Systems vol. 15, 2014, pp. 2368–2377. https://doi.org/10.1109/TITS.2014.233733110.1109/TITS.2014.2337331
  17. [17] O. Oreifej, Z. Liu, “Hon4d: Histogram of oriented 4D normals for activity recognition from depth sequences”, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 716–723. https://doi.org/10.1109/CVPR.2013.9810.1109/CVPR.2013.98
DOI: https://doi.org/10.2478/acss-2020-0007 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
Language: English
Page range: 57 - 61
Published on: Jun 5, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2020 Falah Obaid, Amin Babadi, Ahmad Yoosofan, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.