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Action Key Frames Extraction Using L1-Norm and Accumulative Optical Flow for Compact Video Shot Summarisation

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Advances in Visual Informatics (IVIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10645))

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Abstract

Key frame extraction is an important algorithm for video summarisation, video retrieval, and generating video fingerprint. The extracted key frames should represent a video sequence in a compact way and brief the main actions to achieve meaningful key frames. Therefore, we present a key frames extraction algorithm based on the L1-norm by accumulating action frames via optical flow method. We then evaluate our proposed algorithm using the action accuracy rate and action error rate of the extracted action frames in comparison to user extraction. The video shot summarisation evaluation shows that our proposed algorithm outperforms the-state-of-the-art algorithms in terms of compression ratio. Our proposed algorithm also achieves approximately 100% and 0.91% for best and worst case in terms of action appearance accuracy in human action dataset KTH in the extracted key frames.

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References

  1. Hu, W., Xie, N., Li, L., Zeng, X., Maybank, S.: A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41(6), 797–819 (2011)

    Article  Google Scholar 

  2. Sujatha, C., Mudenagudi, U.: A study on keyframe extraction methods for video summary. In: International Conference on Computational Intelligence and Communication Networks (CICN), pp. 73–77. IEEE (2011)

    Google Scholar 

  3. Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision, vol. 81, no. 1, pp. 674–679 (1981)

    Google Scholar 

  4. Lu, N., Wang, J., Yang, L., Wu, Q.H.: Motion detection based on accumulative optical flow and double background filtering. In: World Congress on Engineering (WCE), IWCE 2007, London, U.K., pp. 602–607. Citeseer (2007)

    Google Scholar 

  5. Zheng, R., Yao, C., Jin, H., Zhu, L., Zhang, Q., Deng, W.: Parallel key frame extraction for surveillance video service in a smart city. PLOS One 10, 1–8 (2015)

    Google Scholar 

  6. Raikwar, S.C., Bhatnagar, C., Jalal, A.S.: A framework for key frame extraction from surveillance video. In: International Conference on Computer and Communication Technology (ICCCT), Allahabad, India, pp. 297–300. IEEE (2014)

    Google Scholar 

  7. Sosa, J.C., Rodríguez, R., Ortega, V.H.G., Hernández, R.: Real-time optical-flow computation for motion estimation under varying illumination conditions. Int. J. Reconfigurable Embed. Syst. (IJRES) 1(1), 25–36 (2012)

    Google Scholar 

  8. Sheena, C.V., Narayanan, N.K.: Key-frame extraction by analysis of histograms of video frames using statistical methods. Procedia Comput. Sci. 70, 36–40 (2015)

    Article  Google Scholar 

  9. Thepade, S.D., Tonge, A.A.: An optimized key frame extraction for detection of near duplicates in content based video retrieval. In: International Conference on Communications and Signal Processing (ICCSP), pp. 1087–1091. IEEE (2014)

    Google Scholar 

  10. Cao, C., Chen, Z., Xie, G., Lei, S.: Key frame extraction based on frame blocks differential accumulation. In: 24th Chinese Control and Decision Conference (CCDC), pp. 3621–3625. IEEE (2012)

    Google Scholar 

  11. Mizher, M.A., Ang, M.C., Mazhar, A.A., Mizher, M.A.: A review of video falsifying techniques and video forgery detection techniques. Int. J. Electron. secur. digit. 9(3), 191–209 (2017). Publisher: Inderscience online

    Google Scholar 

  12. Shi, Y., Yang, H., Gong, M., Liu, X., Xia, Y. :A fast and robust key frame extraction method for video copyright protection. J. Electr. Comput. Eng. 2017, 1–7 (2017). Article no. 1231794

    Google Scholar 

  13. Zamani, N.A., Zahamdin, A.D.M., Abdullah, S.N.H.S., Nordin, M.J.: Sparse representation super-resolution method for enhancement analysis in video forensics. In: 12th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 921–926. IEEE (2012)

    Google Scholar 

  14. Schindler, K., Gool, L.V.: Action snippets: how many frames does human action recognition require? In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)

    Google Scholar 

  15. Abdulameer, M.H., Abdullah, S.N.H.S., Othman, Z.A.: Support vector machine based on adaptive acceleration particle swarm optimization. Sci. World J. 2014, 1–8 (2014). Publisher: Hindawi Publishing Corporation

    Google Scholar 

  16. Claerbout, J.F., Muir, F.: Robust modeling with erratic data. Geophysics 38(5), 826–844 (1973). Publisher: Society of Exploration Geophysicists

    Article  Google Scholar 

  17. Richardson, I.E.: H. 264 and MPEG-4 Video Compression: Video Coding for Next-Generation Multimedia. Wiley, Hoboken (2004)

    Google Scholar 

  18. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  19. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR), vol. 3, pp. 32–36. IEEE (2004)

    Google Scholar 

  20. Hussein, W.A., Sahran, S., Abdullah, S.N.H.S.: A fast scheme for multilevel thresholding based on a modified bees algorithm. Knowl.-Based Syst. 101, 114–134 (2016)

    Article  Google Scholar 

  21. Dang, C., Radha, H.: RPCA-KFE: key frame extraction for video using robust principal component analysis. IEEE Trans. Image Process. 24(11), 3742–3753 (2015)

    Article  MathSciNet  Google Scholar 

  22. Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 3(1), 3 (2007)

    Article  Google Scholar 

  23. de Avila, S.E.F., Lopes, A.P.B., da Luz Jr., A., de Albuquerque Araújo, A.: VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn. Lett. 32(1), 56–68 (2011). Publisher Elsevier B.V.

    Article  Google Scholar 

  24. de Avila, S.E.F., da Luz Jr., A., de Albuquerque Araújo, A., Cord, M.: VSUMM: an approach for automatic video summarization and quantitative evaluation. In: XXI Brazilian Symposium on Computer Graphics and Image Processing, Campo Grande, Brazil, pp. 103–110. IEEE (2008)

    Google Scholar 

  25. Vishwakarma, D.K., Rawat, P., Kapoor, R.: Human activity recognition using gabor wavelet transform and ridgelet transform. Procedia Comput. Sci. 57, 630–636 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Manar Abduljabbar Ahmad Mizher & Mei Choo Ang

  2. Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Siti Norul Huda Sheikh Abdullah

  3. Industrial Centre of Innovation in Industrial Design, Sirim Berhad, Bukit Jalil, Malaysia

    Kok Weng Ng

Authors
  1. Manar Abduljabbar Ahmad Mizher
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  2. Mei Choo Ang
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  3. Siti Norul Huda Sheikh Abdullah
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  4. Kok Weng Ng
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Corresponding author

Correspondence to Mei Choo Ang .

Editor information

Editors and Affiliations

  1. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Halimah Badioze Zaman

  2. University of Cambridge, Cambridge, United Kingdom

    Peter Robinson

  3. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  4. National Central University, Jhongli, Taiwan

    Timothy K. Shih

  5. Carlos III University of Madrid, Madrid, Spain

    Sergio Velastin

  6. Toyo University, Kawagoe, Japan

    Tada Terutoshi

  7. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Azizah Jaafar

  8. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Nazlena Mohamad Ali

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Mizher, M.A.A., Ang, M.C., Abdullah, S.N.H.S., Ng, K.W. (2017). Action Key Frames Extraction Using L1-Norm and Accumulative Optical Flow for Compact Video Shot Summarisation. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2017. Lecture Notes in Computer Science(), vol 10645. Springer, Cham. https://doi.org/10.1007/978-3-319-70010-6_34

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  • DOI: https://doi.org/10.1007/978-3-319-70010-6_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70009-0

  • Online ISBN: 978-3-319-70010-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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