[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Open-Set Recognition

  • Chapter
  • First Online:
Computer Science in Sport
  • 667 Accesses

Abstract

The focus of this chapter is on open-set recognition (OSR) problems in Sports Sciences. It introduces the OSR concept, and possible application scenarios are highlighted, including Sports video analysis based on event and action recognition. This chapter also overviews relevant studies in the OSR area. A few studies have been identified in the area of Sports Sciences, which opens opportunities for future work.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 54.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 71.39
Price includes VAT (United Kingdom)
  • 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

  • Bendale, A., & Boult, T. E. (2016). Towards open set deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1563–1572).

    Google Scholar 

  • Boult, T. E., Cruz, S., Dhamija, A. R., Gunther, M., Henrydoss, J., & Scheirer, W. J. (2019, July). Learning and the unknown: Surveying steps toward open world recognition. In Proceedings of the AAAI conference on artificial intelligence (vol. 33, no. 1, pp. 9801–9807).

    Google Scholar 

  • Burns, D., Boyer, P., Arrowsmith, C., & Whyne, C. (2022). Personalized activity recognition with deep triplet embeddings. Sensors, 22(14), 5222.

    Article  Google Scholar 

  • Cardoso, D. O., Gama, J., & França, F. M. (2017). Weightless neural networks for open set recognition. Machine Learning, 106(9), 1547–1567.

    Article  Google Scholar 

  • de Oliveira Werneck, R., Raveaux, R., Tabbone, S., & da Silva Torres, R. (2019). Learning cost function for graph classification with open-set methods. Pattern Recognition Letters, 128, 8–15.

    Article  Google Scholar 

  • Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 1–26.

    Google Scholar 

  • Ganesh, Y., Sri Teja, A., Munnangi, S. K., & Rama Murthy, G. (2019, June). A novel framework for fine grained action recognition in soccer. In International work-conference on artificial neural networks (pp. 137–150). Springer.

    Google Scholar 

  • Ge, Z., Demyanov, S., Chen, Z., & Garnavi, R. (2017). Generative openmax for multi-class open set classification. arXiv preprint arXiv:1707.07418.

    Google Scholar 

  • Geng, C., Huang, S. J., & Chen, S. (2020). Recent advances in open set recognition: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10), 3614–3631.

    Article  Google Scholar 

  • Goes, F. R., Meerhoff, L. A., Bueno, M. J. O., Rodrigues, D. M., Moura, F. A., Brink, M. S., et al. (2021). Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review. European Journal of Sport Science, 21(4), 481–496.

    Article  CAS  Google Scholar 

  • Liang, S., Li, Y., & Srikant, R. (2017). Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690.

    Google Scholar 

  • Mendes Júnior, P. R., De Souza, R. M., Werneck, R. D. O., Stein, B. V., Pazinato, D. V., de Almeida, W. R., et al. (2017). Nearest neighbors distance ratio open-set classifier. Machine Learning, 106(3), 359–386.

    Article  Google Scholar 

  • Naik, B. T., Hashmi, M. F., & Bokde, N. D. (2022). A comprehensive review of computer vision in sports: Open issues, future trends and research directions. Applied Sciences, 12(9), 4429.

    Article  CAS  Google Scholar 

  • Neira, M. A. C., Júnior, P. R. M., Rocha, A., & Torres, R. D. S. (2018). Data-fusion techniques for open-set recognition problems. IEEE Access, 6, 21242–21265.

    Article  Google Scholar 

  • Oza, P., & Patel, V. M. (2019). C2ae: Class conditioned auto-encoder for open-set recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2307–2316).

    Google Scholar 

  • Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. Springerplus, 5(1), 1410.

    Article  Google Scholar 

  • Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., & Boult, T. E. (2012). Toward open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1757–1772.

    Article  Google Scholar 

  • Sun, X., Yang, Z., Zhang, C., Ling, K. V., & Peng, G. (2020). Conditional gaussian distribution learning for open set recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13480–13489).

    Google Scholar 

  • Suzuki, G., Takahashi, S., Ogawa, T., & Haseyama, M. (2018, October). Team tactics estimation in soccer videos via deep extreme learning machine based on players formation. In 2018 IEEE 7th global conference on consumer electronics (GCCE) (pp. 116–117). IEEE.

    Google Scholar 

  • Wu, L., Yang, Z., Wang, Q., Jian, M., Zhao, B., Yan, J., & Chen, C. W. (2020). Fusing motion patterns and key visual information for semantic event recognition in basketball videos. Neurocomputing, 413, 217–229.

    Article  Google Scholar 

  • Yoon, Y., Yu, J., & Jeon, M. (2019). Spatio-temporal representation matching-based open-set action recognition by joint learning of motion and appearance. IEEE Access, 7, 165997–166010.

    Article  Google Scholar 

  • Yoshihashi, R., Shao, W., Kawakami, R., You, S., Iida, M., & Naemura, T. (2019). Classification-reconstruction learning for open-set recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4016–4025).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ricardo da Silva Torres .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

da Silva Torres, R. (2024). Open-Set Recognition. In: Memmert, D. (eds) Computer Science in Sport. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-68313-2_26

Download citation

Publish with us

Policies and ethics