NLP based Video Summarisation using Machine Learning

Authors

  • Prof. Kapil Hande  Department of Computer Science & Engineering, Priyadarshini Bhagwati College Of Engineering,Nagpur, Nagpur, Maharashtra, India
  • Hrushi Karlekar  Student, Department of Computer Science & Engineering, Priyadarshini Bhagwati College Of Engineering,Nagpur, Nagpur, Maharashtra, India
  • Pranit Yeole  Student, Department of Computer Science & Engineering, Priyadarshini Bhagwati College Of Engineering,Nagpur, Nagpur, Maharashtra, India
  • Aditya Likhar  Student, Department of Computer Science & Engineering, Priyadarshini Bhagwati College Of Engineering,Nagpur, Nagpur, Maharashtra, India
  • Himanshu Rangari  Student, Department of Computer Science & Engineering, Priyadarshini Bhagwati College Of Engineering,Nagpur, Nagpur, Maharashtra, India

DOI:

https://doi.org/10.32628/IJSRSET2310265

Keywords:

Video Summarizer, Text Summarizer, Long Short Term Memory, Machine Learning, Summarizer, Abstractive, Extractive

Abstract

More people are capturing their daily lives with video data, because to the wide availability of recording equipment. However, the sheer volume of video content makes it more challenging to manage, especially for lengthy movies like security or CCTV footage. For a richer and more succinct condensing of the film will result from automatically detecting the key sections and frames of larger videos and providing them with captions. Users still need to spend time searching or navigating through a summarized video. To extract a shortened version of the footage's information into text form, automatic video summarizing has been proposed. With simply a text summary, the suggested system provides a quick semantic understanding of a lengthy film using LSTM model and the summary can be taken in 3 major different languages (English, Hindi, & Marathi).

References

  1. H. W. J. Z. D. S. S. Zhang, "An integrated system for contentbased video retrieval and browsing," Pattern recognition, vol. 30(4), p. 643–658, 2018.
  2. B. C. W. G. K. S. F. Gong, "Diverse sequential subset selection for supervised video summarization," In: NIPS, 2014.
  3. P. R. Y. Y. Y. Mundur, " Keyframe-based video summarization using delaunay clustering," International Journal on Digital Libraries, vol. 6(2), p. 219–232, 2016.
  4. D. H. G. C. T. Liu, "A hierarchical visual model for video object summarization," IEEE transactions on pattern analysis and machine intelligence , vol. 32(12), p. 2178–2190, 2020.
  5. Y. G. J. G. K. Lee, "Discovering important people and objects for egocentric video summarization," In: CVPR, 2012.
  6. M. L. a. S. T. B. Mahasseni, "Unsupervised Video Summarization with Adversarial LSTM Networks," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA,, no. doi: 10.1109/CVPR.2017.318., pp. pp. 2982-2991, 2017.
  7. S. J. S. u. H. M. A. H. a. M. U. G. K. M. Z. Khan, "Video Summarization using CNN and Bidirectional LSTM by Utilizing Scene Boundary Detection,," International Conference on Applied and Engineering Mathematics (ICAEM), Taxila, Pakistan, no. doi: 10.1109/ICAEM.2019.8853663., pp. pp. 197-202, 2019.
  8. D. Sahrawat et al., "Video Summarization using Global Attention with Memory Network and LSTM," 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), Singapore,, no. doi: 10.1109/BigMM.2019.00-20., pp. pp. 231-236,, 2019.
  9. R. M. a. G. S. C. R. Agyeman, "Soccer Video Summarization Using Deep Learning," 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA,, no. doi: 10.1109/MIPR.2019.00055., pp. pp. 270-273, 2019.
  10. K. M. A. U. Z. C. S. W. B. a. V. H. C. d. A. T. Hussain, "Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM," in IEEE Transactions on Industrial Informatics, Vols. vol. 16, no. 1, no. doi: 10.1109/TII.2019.2929228., pp. pp. 77-86, Jan. 2020.
  11. G. Y. a. N. Ikizler-Cinbis, "Unsupervised Video Summarization with Independently Recurrent Neural Networks," 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey,, no. doi: 10.1109/SIU.2019.8806603, pp. pp. 1-4, 2019.
  12. F. S. J. C. F. Gers, "Learning to forget: Continual prediction with lstm," Neural computation, vol. 12(10), p. 2451–2471, 2020.
  13. S. S. J. Hochreiter, "Long short-term memory," Neural computation, vol. 9(8), p. 1735–1780, 2019.
  14. A. N. NLP, "What is NLP Text Summarization: Benefits & Use Cases," Accern NoCode NLP, 28 September 2022. [Online]. Available: https://accern.com/blog/nlp-text-summarization/. [Accessed 13 April 2023].
  15. N. B. A. G. Wajdi Homaid Alquliti, "Convolutional Neural Network based for Automatic Text Summarization," International Journal of Advanced Computer Science and Applications(IJACSA), vol. Volume 10, no. Issue 4, 2019., 2019.

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Published

2023-04-30

Issue

Section

Research Articles

How to Cite

[1]
Prof. Kapil Hande, Hrushi Karlekar, Pranit Yeole, Aditya Likhar, Himanshu Rangari "NLP based Video Summarisation using Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.456-461, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRSET2310265