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GPUs and Multicore CPUs Implementations of a Static Video Summarization

  • Suellen S. Almeida
  • Edward Cayllahua-Cahuina
  • Arnaldo de A. Araújo
  • Guillermo Cámara-Chávez
  • David Menotti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8827)

Abstract

The fast evolution of digital media, in special digital videos, has created an exponential growth of data, increasing the storage and transmission cost and the video content retrieve information complexity. Video summarization has been proposed to circumvent some of these issues and also serves as a pre-processing step in many video applications. In this paper, a static video summarization algorithm is studied and in order to reduce its high execution time, parallelizations using Graphics Processor Units (GPUs) and multicore CPUs are proposed. We also explore a hybrid approach combining both hardware to maximize the performance. The experiments were performed using 120 videos varying frame resolution and video length and the results showed that the hybrid and the multicore CPUs versions reached the best executions times, achieving 4× speedup in average.

Keywords

Video summarization GPUs Multicore-CPUs Parallel algorithms 

References

  1. 1.
    Almeida, J., Leite, N.J., da, S., Torres, R.: Vison: VIdeo summarization for ONline applications. Pattern Recognition Letters 33(4), 397–409 (2012)CrossRefGoogle Scholar
  2. 2.
    Camara Chavez, G., et al.: Shot boundary detection by a hierarchical supervised approach. In: IWSSIP, pp. 197–200 (2007)Google Scholar
  3. 3.
    Cayllahua-Cahuina, E.J.Y., Cámara-Chávez, G.: A new method for static video summarization using local descriptors and video. In: SIBGRAPI (2013)Google Scholar
  4. 4.
    Cheung, N.M., Fan, X., Au, O., Kung, M.C.: Video coding on multicore graphics processors. IEEE Signal Processing Magazine 27(2), 79–89 (2010)CrossRefGoogle Scholar
  5. 5.
    Ciocca, G., Schettini, R.: Erratum to: An innovative algorithm for key frame extraction in video summarization. JRTIP 8(2), 225–225 (2013)Google Scholar
  6. 6.
    Clemons, J., et al.: Effex: An embedded processor for computer vision based feature extraction. In: DAC, pp. 1020–1025 (2011)Google Scholar
  7. 7.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)Google Scholar
  8. 8.
    De Boor, C.: A practical guide to splines, vol. 27. Springer, New York (1978)CrossRefzbMATHGoogle Scholar
  9. 9.
    Evangelio, R., et al.: Video indexing and summarization as a tool for privacy protection. In: DSP, pp. 1–6 (2013)Google Scholar
  10. 10.
    Furini, M., et al.: On using clustering algorithms to produce video abstracts for the web scenario. In: CCNC, pp. 1112–1116 (2008)Google Scholar
  11. 11.
    Guan, G., et al.: Video summarization with global and local features. In: ICMEW, pp. 570–575 (2012)Google Scholar
  12. 12.
    Holub, P., et al.: Gpu-accelerated DXT and JPEG compression schemes for low-latency network transmissions of HD, 2k, and 4k video. FGCS 29(8) (2013)Google Scholar
  13. 13.
    Kuanar, S.K., et al.: Video key frame extraction through dynamic delaunay clustering with a structural constraint. JVCI 24(7), 1212–1227 (2013)Google Scholar
  14. 14.
    Li, P., et al.: Interactive image/video retexturing using GPU parallelism. Computers & Graphics 36(8), 1048–1059 (2012)CrossRefGoogle Scholar
  15. 15.
    Linde, Y., Buzo, A., Gray, R.: An algorithm for vector quantizer design. IEEE Transactions on Communications 28(1), 84–95 (1980)CrossRefGoogle Scholar
  16. 16.
    NVIDIA: NVIDIA CUDA Video Decoder. NVIDIA (2010)Google Scholar
  17. 17.
    Omitted: Parallels implementation for temporal video summarization and databases (2014), https://github.com/omitted/tsumm
  18. 18.
    Pelleg, D., Moore, A.W.: X-means: Extending k-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734 (2000)Google Scholar
  19. 19.
    Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st edn. Addison-Wesley Professional (2010)Google Scholar
  20. 20.
    Sinha, S.N., et al.: Feature tracking and matching in video using programmable graphics hardware. Machine Vision and Applications 22(1), 207–217 (2011)CrossRefGoogle Scholar
  21. 21.
    Won, J.U., et al.: Correlation based video-dissolve detection. In: ITRE, pp. 104–107 (2003)Google Scholar
  22. 22.
    Yang, J., et al.: Evaluating bag-of-visual-words representations in scene classification. In: MIR, pp. 197–206 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Suellen S. Almeida
    • 1
  • Edward Cayllahua-Cahuina
    • 1
  • Arnaldo de A. Araújo
    • 1
  • Guillermo Cámara-Chávez
    • 2
  • David Menotti
    • 2
  1. 1.Computer Science DepartmentFederal University of Minas GeraisBrazil
  2. 2.Computing DepartmentFederal University of OuroPretoBrazil

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