Skip to main content
SpringerLink
Log in

Emperor Penguin optimized event recognition and summarization for cricket highlight generation

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Cricket highlight generation is the process of summarizing a full-length video to a shortened form which should preserve the important moments present in the original video. In this paper, a new approach has been proposed for recognizing the key events and summarization. Audio features are initially used for extracting the excitement clips. Then, the important events like replay, players, umpires, spectators, and players gathering are extracted from each clip. Here, a hybrid deep neural network with Emperor Penguin optimization (HDNN-EPO) is proposed for labeling the excitement concepts presented in the cricket video based on the observed events automatically. These labeled concepts are then selected based on the importance degree and concatenated in temporal order to form highlights. The efficiency of the proposed method is proved through the experimental results and it outperforms the other existing approaches. Also, the extracted highlights have been compared with the manually-generated highlights by the sports television channel.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

References

  1. Shih, H.C.: A survey of content-aware video analysis for sports. IEEE Trans. Circuits Syst. Video Technol. 28(5), 1212–1231 (2018)

    Article  Google Scholar 

  2. Wang, L., Wang, Z., Qiao, Y., Van Gool, L.: Transferring deep object and scene representations for event recognition in still images. Int. J. Comput. Vision 126, 09 (2017)

    Article  MathSciNet  Google Scholar 

  3. Renò, V., Mosca, N., Nitti, M., D’Orazio, T., Guaragnella, C., Campagnoli, D., Prati, A., Stella, E.: A technology platform for automatic high-level tennis game analysis. Comput. Vision Image Understand. 159, 01 (2017)

    Article  Google Scholar 

  4. Nguyen, D.T., Jung, J.E.: Real-time event detection for online behavioral analysis of big social data. Future Gener. Comput. Syst. 1(66), 137–145 (2017)

    Article  Google Scholar 

  5. Ravi, A., Venugopal, H., Paul, S., Tizhoosh, H.R.: A dataset and preliminary results for umpire pose detection using SVM classification of deep features. In:  2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1396–1402. IEEE (2018)

  6. Ellappan, V., Rajasekaran, R.: Event recognition and classification in sports video. In:  2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM), pp. 182–187. IEEE (2017)

  7. Shukla P., Sadana, H., Bansal, A., Verma, D., Elmadjian, C., Raman, B., Turk, M.: Automatic cricket highlight generation using event-driven and excitement-based features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1800–1808 (2018)

  8. Javed A.: Event driven video summarization for sports. PhD diss., University of Engineering and Technology, Taxila, Pakistan, (2016)

  9. Kolekar, M.H., Sengupta, S.: Bayesian network-based customized highlight generation for broadcast soccer videos. IEEE Trans. Broadcast. 61(2), 195–209 (2015)

    Article  Google Scholar 

  10. Baijal, A., Cho, J., Lee, W., Ko, B.-S.: Sports highlights generation based on acoustic events detection: a rugby case study. In: 2015 IEEE International Conference on Consumer Electronics (ICCE), pp. 20–23. IEEE (2015)

  11. Zhang J, Yao, J., Wan, X.: Towards constructing sports news from live text commentary. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1361–1371 (2016)

  12. Bhalla, A., Ahuja, A., Pant, P., Mittal, A.: A multimodal approach for automatic cricket video summarization. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 146–150. IEEE (2019)

  13. Baliarsingh, S.K., Vipsita, S., Muhammad, K., Bakshi, S.: Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer. Swarm Evolut. Comput. 48, 262–273 (2019)

    Article  Google Scholar 

  14. Xing, Z.: An improved emperor penguin optimization based multilevel thresholding for color image segmentation. Knowl.-Based Syst 194, 105570 (2020)

    Article  Google Scholar 

  15. Javed, A., Irtaza, S.A., Malik, H., Mahmood, M., Adnan, S.M.: A multimodal framework based on audio-visual features for summarization of cricket videos. IET Image Proc. 13(4), 615–622 (2019)

    Article  Google Scholar 

  16. Kolekar, M.H.: Bayesian belief network-based broadcast sports video indexing. Multimedia Tools Appl. 54(1), 27–54 (2011)

    Article  Google Scholar 

  17. Nasir, M., Javed, A., Irtaza, A., Malik, H., Mahmood, M.: Event detection and summarization of cricket videos. J. Image Graphics 6, 27–32 (2018)

    Article  Google Scholar 

  18. Midhu, K., Padmanabhan, N.A.: Highlight the generation of cricket matches using deep learning. In: Computational Vision and Bio Inspired Computing, pp. 925–936. Springer, Cham (2018)

    Google Scholar 

  19. Chakraborty, S., Thounaojam, D.: A novel shot boundary detection system using hybrid optimization technique. Appl. Intell. 49, 03 (2019)

    Article  Google Scholar 

  20. Javed, A., Bajwa, K.B., Malik, H., Irtaza, A., Mahmood, M.T.: A hybrid approach for summarization of cricket videos. In: 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 1–4. IEEE (2016)

  21. Kolekar, M.H., Sengupta, S.: Semantic concept mining in cricket videos for automated highlight generation. Multimedia Tools Appl. 47(3), 545–579 (2010)

    Article  Google Scholar 

  22. Hari, R., Wilscy, M.: Event detection in cricket videos using intensity projection profile of Umpire gestures. In: 2014 Annual IEEE India Conference (INDICON), pp. 1–6. IEEE (2014)

  23. Merler, M., Mac, K.-N.C., Joshi, D., Nguyen, Q.-B., Hammer, S., Kent, J., Xiong, J., Do, M.N., Smith, J.R., Feris, R.S.: Automatic curation of sports highlights using multimodal excitement features.IEEE Trans. Multimedia 21, 1147–1160 (2019)

    Article  Google Scholar 

  24. F. Hajati and M. Tavakolian, Video Classification Using Deep Autoencoder Network, 01 2020, pp. 508–518

    Google Scholar 

  25. Dhiman, G., Kumar, V.: Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl.-Based Syst. 159, 20–50 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Gujarat Technological University, Ahmedabad, Gujarat, India

    Hansa Shingrakhia & Hetal Patel

  2. ECE Department, Indus University, Ahmedabad, Gujarat, India

    Hansa Shingrakhia

  3. ECE Department, A.D. Patel Institute of Technology and Engineering, Anand, Gujarat, India

    Hetal Patel

Authors
  1. Hansa Shingrakhia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hetal Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hansa Shingrakhia.

Additional information

Communicated by T. Yao.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shingrakhia, H., Patel, H. Emperor Penguin optimized event recognition and summarization for cricket highlight generation. Multimedia Systems 26, 745–759 (2020). https://doi.org/10.1007/s00530-020-00684-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-020-00684-3

Keywords

Search

Navigation