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Towards a Professional Gesture Recognition with RGB-D from Smartphone

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Computer Vision Systems (ICVS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11754))

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Abstract

The goal of this work is to build the basis for a smartphone application that provides functionalities for recording human motion data, train machine learning algorithms and recognize professional gestures. First, we take advantage of the new mobile phone cameras, either infrared or stereoscopic, to record RGB-D data. Then, a bottom-up pose estimation algorithm based on Deep Learning extracts the 2D human skeleton and exports the 3rd dimension using the depth. Finally, we use a gesture recognition engine, which is based on K-means and Hidden Markov Models (HMMs). The performance of the machine learning algorithm has been tested with professional gestures using a silk-weaving and a TV-assembly datasets.

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References

  1. Fang, H., Xie, S., Lu, C.: RMPE: regional multi-person pose estimation (2016)

    Google ScholarĀ 

  2. GĆ¼ler, R.A., Neverova, N., Kokkinos, I.: DensePose: dense human pose estimation in the wild (2018)

    Google ScholarĀ 

  3. Abdulla, W.: Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. GitHub repository (2017)

    Google ScholarĀ 

  4. Pishchulin, L., et al.: DeepCut: joint subset partition and labeling for multi person pose estimation (2015)

    Google ScholarĀ 

  5. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Openpose: realtime multi-person 2D pose estimation using part affinity fields (2018)

    Google ScholarĀ 

  6. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)

    Google ScholarĀ 

  7. Ignatov, A., et al.: AI benchmark: running deep neural networks on android smartphones (2018)

    Google ScholarĀ 

  8. Krafka, K.: Eye tracking for everyone (2016)

    Google ScholarĀ 

  9. Zhang, L., Zhu, G., Shen, P., Song, J.: Learning spatiotemporal features using 3DCNN and convolutional LSTM for gesture recognition. In: ICCV Workshop (2017)

    Google ScholarĀ 

  10. Wang, H., Wang, P., Song, Z., Li, W.: Large-scale multimodal gesture recognition using heterogeneous networks. In: ICCV 2017 Workshop, pp. 3129ā€“3137 (2017)

    Google ScholarĀ 

  11. Wang, P., Li, W., Liu, S., Gao, Z., Tang, C., Ogunbona, P.: Large-scale isolated gesture recognition using convolutional neural networks (2017)

    Google ScholarĀ 

  12. Corradini, A.: Dynamic time warping for off-line recognition of a small gesture vocabulary, pp. 82-89 (2001)

    Google ScholarĀ 

  13. CoupetĆ©, E., Moutarde, F., Manitsaris, S.: Multi-users online recognition of technical gestures for natural human-robot collaboration in manufacturing. Auton. Robot. 43, 1309ā€“1325 (2018)

    ArticleĀ  Google ScholarĀ 

  14. Gillian, N., Paradiso, J.A.: The gesture recognition toolkit. J. Mach. Learn. Res. 15, 3483ā€“3487 (2014)

    MathSciNetĀ  Google ScholarĀ 

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Acknowledgement

The research leading to these results has received funding by the EU Horizon 2020 Research and Innovation Programme under grant agreement No. 820767, CoLLaboratE project, and No. 822336, Mingei project. We acknowledge also the ArƧelik factory and the Museum Haus der Seidenkultur for providing as with the use-cases as well as the Foundation for Research and Technology ā€“ Hellas for contributing to the motion capturing.

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Correspondence to Pablo Vicente MoƱivar .

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MoƱivar, P.V., Manitsaris, S., Glushkova, A. (2019). Towards a Professional Gesture Recognition with RGB-D from Smartphone. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_22

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  • DOI: https://doi.org/10.1007/978-3-030-34995-0_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34994-3

  • Online ISBN: 978-3-030-34995-0

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