Skip to main content
SpringerLink
Log in

Stochastic-Gradient-Descent-Based Max-Margin Early Event Detector

  • Conference paper
  • First Online:
New Trends in Computer Technologies and Applications (ICS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1723))

Included in the following conference series:

  • 660 Accesses

Abstract

Max-margin-based early event detection is first solved by max-margin early event detector (MMED) proposed by Hoai and Torre [10]. In this study, the stochastic gradient descent mechanism is used to replace the quadratic programming solver in [10] to achieve early event detection. Three datasets are tested, including synthetic data, the extended Cohn-Kanade dataset (CK+), and Australian sign language data (ASL). The experimental results show that the proposed approach is feasible, and that the performance is comparable to that obtained in MMED.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

Similar content being viewed by others

References

  1. Shahroudy, A., Ng, T., Gong, Y., Wang, G.: Deep multimodal feature analysis for action recognition in RGB+D videos. IEEE Trans. Pattern Anal. Mach. Intell. 40(5), 1045–1058 (2018)

    Article  Google Scholar 

  2. Tran, D., Yuan, J., Forsyth, D.: Video event detection: from sub-volume localization to spatio-temporal path search. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 404–416 (2014)

    Article  Google Scholar 

  3. Alpaydin, E.: Introduction to Machine Learning, 4th edn. MIT Press, London (2020)

    MATH  Google Scholar 

  4. Huang, D., Yao, S., Wang, Y., De La Torre, F.: Sequential max-margin event detectors. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 410–424. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_27

    Chapter  Google Scholar 

  5. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res. 6(2), 1453–1484 (2005)

    MathSciNet  MATH  Google Scholar 

  6. Azorin-López, J., Saval-Calvo, M., Fuster-Guilló, A., Oliver-Albert, A.: A predictive model for recognizing human behaviour based on trajectory representation. In: Proceedings of International Joint Conference on Neural Networks (IJCNN) (2014)

    Google Scholar 

  7. Wang, J., Wu, Y.: Learning maximum margin temporal warping for action recognition. In: Proceedings of ICCV 2013, pp. 2688-2695 (2013)

    Google Scholar 

  8. Su, L., Sato, Y.: Early facial expression recognition using early RankBoost. In: Proceedings of FG 2013, pp. 1–7 (2013)

    Google Scholar 

  9. Xie, L., Zhao, J., Wei, H., Zhang, K., Pang, G.: Online kernel-based structured output SVM for early expression detection. IEEE Signal Process. Lett. 26(9), 1305–1309 (2019)

    Article  Google Scholar 

  10. Hoai, M., De la Torre, F.: Max-margin early event detectors. Int. J. Comput. Vision 107(2), 191–202 (2013). https://doi.org/10.1007/s11263-013-0683-3

    Article  MathSciNet  Google Scholar 

  11. Lu, S., Jin, Z.: Improved stochastic gradient descent algorithm for SVM. Int. J. Recent Eng. Sci. (IJRES) 4(4), 28–31 (2017)

    Google Scholar 

  12. Shalev-Shwartz, S., Singer, Y., Srebro, N., Cotter, A.: Pegasos: primal estimated sub-GrAdient SOlver for SVM. Math. Program 127, 3–30 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  13. Guha, T., Ward, R.K.: Learning sparse representations for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1576–1588 (2012)

    Article  Google Scholar 

  14. Cao, Y., et al.: Recognize human activities from partially observed videos. In: Proceedings of CVPR 2013, pp. 2658–2665 (2013)

    Google Scholar 

  15. Yang, Z.F., Hung, C.T.: Feedback max-margin early event detector. In: Proceedings of 8th International Conference on Applied System Innovation (ICASI), pp. 115–118 (2022)

    Google Scholar 

  16. Yang, Z.F., Lin, Y.C.: Reduction in number of constraints in max-margin early event detectors. Pattern Recogn. 91, 111–122 (2019)

    Article  Google Scholar 

  17. Xu, Z., Yang, Y., Hauptmann, A.G.: A discriminative CNN video representation for event detection. In: Proceedings of CVPR 2015 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, 23741, Taiwan, R.O.C.

    Hong-Wen Wang, Dai-Yi Chiu, Wei-Chung Chan & Zhi-Fang Yang

Authors
  1. Hong-Wen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dai-Yi Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei-Chung Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhi-Fang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi-Fang Yang .

Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan

    Sun-Yuan Hsieh

  2. National Taipei University of Business, Taoyuan, Taiwan

    Ling-Ju Hung

  3. CNRS and University of Bordeaux, Talence, France

    Ralf Klasing

  4. National Taitung University, Taitung, Taiwan

    Chia-Wei Lee

  5. National Taipei University of Business, Taoyuan, Taiwan

    Sheng-Lung Peng

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, HW., Chiu, DY., Chan, WC., Yang, ZF. (2022). Stochastic-Gradient-Descent-Based Max-Margin Early Event Detector. In: Hsieh, SY., Hung, LJ., Klasing, R., Lee, CW., Peng, SL. (eds) New Trends in Computer Technologies and Applications. ICS 2022. Communications in Computer and Information Science, vol 1723. Springer, Singapore. https://doi.org/10.1007/978-981-19-9582-8_48

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-9582-8_48

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-9581-1

  • Online ISBN: 978-981-19-9582-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation