Skip to main content
SpringerLink
Log in

The Gesture Detection Algorithm Based on 3-DCGAN Range Estimation in FMCW Radar System

  • Conference paper
  • First Online:
Wireless and Satellite Systems (WiSATS 2019)

Included in the following conference series:

  • 1529 Accesses

Abstract

Recently, hand gesture detection has gradually become a research hotspot. We propose a Region-based Faster Convolutional Neural Network (F-RCNN) gesture detection method based on Frequency Modulated Continuous Wave (FMCW) radar using 3-Dimensions Deep Convolutional Generative Adversarial Networks (3-DCGAN). Specifically, this paper adopts FMCW radar for hand gesture data acquisition, and estimates the distance of the hand gesture using the regularity of the change of echo frequency and emission frequency of radar signals. Then the semantic label maps of the generated images of distance are sent to the 3-DCGAN to extend datasets. After that, the original images and the images generated by the 3-DCGAN are simultaneously sent to F-RCNN for training. The results show that the proposed approach increases the mAP by 3% compared to the baseline F-RCNN. Besides, the proposed method not only effectively solves the problem of small amount of hand gesture data, but also the manpower and material resources consumed by collecting data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jia, M., Gu, X., Guo, Q., Xiang, W., Zhang, N.: Broadband hybrid satellite-terrestrial communication systems based on cognitive radio toward 5G. IEEE Wirel. Commun. 23(6), 96–106 (2016)

    Article  Google Scholar 

  2. Jia, M., Liu, X., Gu, X., Guo, Q.: Joint cooperative spectrum sensing and channel selection optimization for satellite communication systems based on cognitive radio. Int. J. Satell. Commun. Network. 35(2), 139–150 (2017)

    Article  Google Scholar 

  3. Jia, M., Liu, X., Yin, Z., Guo, Q., Gu, X.: Joint cooperative spectrum sensing and spectrum opportunity for satellite cluster communication networks. Ad Hoc Netw. 58, 231–238 (2016)

    Article  Google Scholar 

  4. Hjelmas, E., Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83(3), 236–274 (2001)

    Article  Google Scholar 

  5. Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 37(3), 311–324 (2007)

    Article  Google Scholar 

  6. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a reference survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)

    Article  Google Scholar 

  7. Gavrila, D.M.: The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73(1), 82–98 (1999)

    Article  Google Scholar 

  8. Raj, B., Kalgaonkar, K., Harrison, C., Dietz, P.: Ultrasonic doppler sensing in HCI. IEEE Pervasive Comput. 11(2), 24–29 (2012)

    Article  Google Scholar 

  9. Wan, Q., Li, Y., Li, C., Pal, R.: Gesture recognition for smart home applications using portable radar sensors. In: IEEE Conference on Engineering in Medicine and Biology Society, pp. 6414–6417, August 2014

    Google Scholar 

  10. Molchanov, P., Gupta, S., Kim, K., Pulli, K.: Multi-sensor system for driver’s hand-gesture recognition. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1–8. IEEE (2015)

    Google Scholar 

  11. Molchanov, P., Gupta, S., Kim, K., et al.: Short-range FMCW monopulse radar for hand-gesture sensing. In: 2015 IEEE Radar Conference (RadarCon), pp. 1491–1496. IEEE (2015)

    Google Scholar 

  12. Molchanov, P., Gupta, S., Kim, K., et al.: Multi-sensor system for driver’s hand-gesture recognition. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1–8. IEEE (2015)

    Google Scholar 

  13. Wang, S., Song, J., Lien, J., Poupyrev, I., Hilliges, O.: Interacting with soli: exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum. In: Proceedings of the 29th Annual Symposium on User Interface Software and Technology, pp. 851–860. ACM (2016)

    Google Scholar 

  14. Molchanov, P., Gupta, S., Kim, K., et al.: Short-range FMCW monopulse radar for hand-gesture sensing. In: 2015 IEEE Radar Conference (RadarCon) on Automatic Face and Gesture Recognition (FG), vol. 5, pp. 1–6. IEEE (2015)

    Google Scholar 

  15. Piper, S.O.: Homodyne FMCW radar range resolution effects with sinusoidal nonlinearities in the frequency sweep. In: Proceedings of IEEE International Radar Conference, pp. 563–567 (1995)

    Google Scholar 

  16. Molchanov, P., Gupta, S., Kim, K., Pulli, K.: Multi-sensor system for driver’s hand-gesture recognition. In: AFGR (2015)

    Google Scholar 

  17. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. (2016)

    Google Scholar 

  18. Denton, E., Chintala, S., Szlam, A., Fergus, R.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems (NIPS) (2015)

    Google Scholar 

  19. Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., Belongie, S.: Stacked generative adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  20. Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: IEEE International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

  21. Zhang, H., et al.: StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  22. Wang, T., Liu, M., Zhu, J., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: European Conference on Computer Vision (ECCV) (2017)

    Google Scholar 

  23. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 37, 1904–1916 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Xiuqian Jia, Yong Wang, Mu Zhou & Zengshan Tian

Authors
  1. Xiuqian Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zengshan Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiuqian Jia .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Min Jia

  2. Harbin Institute of Technology, Harbin, China

    Qing Guo

  3. Harbin Institute of Technology, Harbin, China

    Weixiao Meng

Rights and permissions

Reprints and permissions

Copyright information

© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jia, X., Wang, Y., Zhou, M., Tian, Z. (2019). The Gesture Detection Algorithm Based on 3-DCGAN Range Estimation in FMCW Radar System. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-030-19153-5_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-19153-5_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19152-8

  • Online ISBN: 978-3-030-19153-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation