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Non-overlapped Multi-source Surveillance Video Coding Using Two-Layer Knowledge Dictionary

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Advances in Multimedia Information Processing -- PCM 2015 (PCM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9314))

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Abstract

In multi-source surveillance videos, a large number of moving objects are captured by different surveillance cameras. Although the regions that each camera covers are seldom overlapped, similarities of these objects among different videos still result in tremendous global object redundancy. Coding each source in an independent way for multi-source surveillance videos is inefficient due to the ignoring of correlation among different videos. Therefore, a novel coding framework for multi-source surveillance videos using two-layer knowledge dictionary is proposed. By analyzing the characteristics of multi-source surveillance videos in large scale of spatio and time space, a two-layer dictionary is built to explore the global object redundancy. Then, a dictionary-based coding method is developed for moving objects. For any object in multi-source surveillance videos, only some pose parameters and sparse coefficients are required for object representation and reconstruction. The experiment with two simulated surveillance videos has demonstrated that the proposed coding scheme can achieve better coding performance than the main profile of HEVC and can preserve better visual quality.

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References

  1. Wiegand, T., Sullivan, G.J., Bjøntegaard, G., Luthra, A.: Overview of the H.264/AVC video coding standard. IEEE Trans. Circuits Syst. Video Technol. 13(7), 560–576 (2003)

    Article  Google Scholar 

  2. Sullivan, G.J., Ohm, J., Han, W., Wiegand, T.: Overview of the high efficiency video coding(HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012)

    Article  Google Scholar 

  3. Paul, M., Lin, W. et al.: A Long-term reference frame for hierarchical B-picture-based video coding. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1729–1742 (2014)

    Article  MathSciNet  Google Scholar 

  4. Zhang, X., Huang, T., Tian, Y., Gao, W.: Background-modeling-based adaptive prediction for surveillance video coding. IEEE Trans. Image Process. 23(2), 769–784 (2014)

    Article  MathSciNet  Google Scholar 

  5. Zhang, X., Tian, Y., Huang, T., Dong, W., Gao, W.: Optimizing the hierarchical prediction and coding in HEVC for surveillance and conference videos with background modeling. IEEE Trans. Image Process. 23(10), 4511–4526 (2014)

    Article  MathSciNet  Google Scholar 

  6. Ng, K., Wu, Q., Chan, S., Shum, H.: Object-Based Coding for plenoptic videos. IEEE Trans. Circuits Syst. Video Technol. 20(4), 548–562 (2010)

    Article  Google Scholar 

  7. Tsai, T., Lin, C.: Exploring contextual redundancy in improvingobject-based video coding for video sensornetworks surveillance. IEEE Trans. Multimedia 14(3), 669–682 (2012)

    Article  Google Scholar 

  8. Yue, H., Sun, X., Yang, J., Wu, F.: Cloud-based image coding for mobile devices—toward thousands to one compression. IEEE Trans. Multimedia 15(4), 845–857 (2013)

    Article  Google Scholar 

  9. Shi, Z., Sun, X., Wu, F.: Feature-based image set compression. In: IEEE International Conference on Multimedia and Expo, pp. 1–6 (2013)

    Google Scholar 

  10. Xiao, J., Chen, Y., Hu, J., Hu, R.: Global coding of multi-source surveillance video data. In: Data Compression Conference, pp. 33–42 (2015)

    Google Scholar 

  11. Xiao, J., Liang, L., Hu, J., Chen, Y., Hu, R.: Exploiting global redundancy in big surveillance video data for high efficient coding. J. Cluster Comput. (2014)

    Google Scholar 

  12. Tan, T.N., Sullivan, G.D., Baker, K.D.: Model-based localization and recognition of road vehicles. Int. J. Comput. Vis. 27(1), 5–25 (1998)

    Article  Google Scholar 

  13. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing over-complete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  14. Liu, Q., Liang, D., Song, Y., et al.: Augmented Lagrangian based sparse representation method with dictionary updating for image deblurring. SIAM J. Imaging Sci. 6(3), 1689–1718 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  15. Liu, Q., Wang, S., Ying, L., et al.: Adaptive dictionary learning in sparse gradient domain for image recovery. IEEE Trans. Image Process. 22(12), 4652–4663 (2013)

    Article  MathSciNet  Google Scholar 

  16. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was partly supported by the China Postdoctoral Science Foundation (2014M562058), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry ([2014]1685), Fundamental Research Funds for the Central Universities (2042014kf0025, 2042014kf0286), EU FP7 QUICK project (PIRSES-GA-2013-612652).

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Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan, China

    Yu Chen, Jing Xiao, Liang Liao & Ruimin Hu

  2. Collaborative Innovation Center of Geospatial Technology, Wuhan, China

    Jing Xiao & Ruimin Hu

Authors
  1. Yu Chen
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  2. Jing Xiao
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  3. Liang Liao
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  4. Ruimin Hu
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Corresponding author

Correspondence to Jing Xiao .

Editor information

Editors and Affiliations

  1. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  2. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Jitao Sang

  3. ICU, IVY Lab, KAIST, Daejeon, Korea (Republic of)

    Yong Man Ro

  4. KAIST, Daejeon, Korea (Republic of)

    Junmo Kim

  5. College of Computer Science, Zhejiang University, Hangzhou, China

    Fei Wu

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Chen, Y., Xiao, J., Liao, L., Hu, R. (2015). Non-overlapped Multi-source Surveillance Video Coding Using Two-Layer Knowledge Dictionary. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_68

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  • DOI: https://doi.org/10.1007/978-3-319-24075-6_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24074-9

  • Online ISBN: 978-3-319-24075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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