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The big data analytics and applications of the surveillance system using video structured description technology

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Abstract

Recently, the video data has very huge volume, taking one city for example, thousands of cameras are built of which each collects high-definition video over 24–48 GB every day with the rapidly growth; secondly, data collected includes variety of formats involving multimedia, images and other unstructured data; furthermore the valuable information contains in only a few frames called key frames of massive video data; and the last problem caused is how to improve the processing velocity of a large amount of original video with computers, so as to enhance the crime prediction and detection effectiveness of police and users. In this paper, we conclude a novel architecture for next generation public security system, and the “front + back” pattern is adopted to address the problems brought by the redundant construction of current public security information systems which realizes the resource consolidation of multiple IT resources, and provides unified computing and storage environment for more complex data analysis and applications such as data mining and semantic reasoning. Under the architecture, we introduce cloud computing technologies such as distributed storage and computing, data retrieval of huge and heterogeneous data, provide multiple optimized strategies to enhance the utilization of resources and efficiency of tasks. This paper also presents a novel strategy to generate a super-resolution image via multi-stage dictionaries which are trained by a cascade training process. Extensive experiments on image super-resolution validate that the proposed solution can get much better results than some state-of-the-arts ones.

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References

  1. Luo, X., Xu, Z., Yu, J., Chen, X.: Building Association Link Network for Semantic Link on Web Resources. IEEE transactions on automation science and engineering 8(3), 482–494 (2011)

    Article  Google Scholar 

  2. Z. Xu et al. Crowdsourcing based Description of Urban Emergency Events using Social Media Big Data. IEEE Transactions on Cloud Computing, doi:10.1109/TCC.2016.2517638

  3. Hu, C., Xu, Z., et al.: Semantic Link Network based Model for Organizing Multimedia Big Data. IEEE Transactions on Emerging Topics in Computing 2(3), 376–387 (2014)

    Article  MathSciNet  Google Scholar 

  4. J. Xiao, L. Liao, J. Hu, Y. Chen, R. Hu. Exploiting global redundancy in big surveillance video data for efficient coding. Cluster Computing, June 2015, 18(1):531-540, 2015

  5. Z. Xu, L. Mei, and C. Hu. Video Structured Description Technology based Intelligence Analysis of Surveillance Videos for Public Security Applications. Multimedia Tools and Applications, doi:10.1007/s11042-015-3112-5

  6. Xu, Z., et al.: Knowle: a Semantic Link Network based System for Organizing Large Scale Online News Events. Future Generation Computer Systems 43–44, 40–50 (2015)

    Article  Google Scholar 

  7. Wei, X., Luo, X., Li, Q., Zhang, J., Xu, Z.: Online Comment-based Hotel Quality Automatic Assessment using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map. IEEE Transactions on Fuzzy Systems 23(1), 72–84 (2015)

    Article  Google Scholar 

  8. Xu, Z., et al.: Mining Temporal Explicit and Implicit Semantic Relations between Entities using Web Search Engines. Future Generation Computer Systems 37, 468–477 (2014)

    Article  Google Scholar 

  9. Xu, Z., et al.: Semantic based representing and organizing surveillance big data using video structural description technology. The Journal of Systems and Software 102, 217–225 (2015)

    Article  Google Scholar 

  10. Xu, Z., et al.: Generating Temporal Semantic Context of Concepts Using Web Search Engines. Journal of Network and Computer Applications 43, 42–55 (2014)

    Article  Google Scholar 

  11. Y, Yan and L. Huang. Large Scale Image Processing Cloud. The fifth international conference on cloud computing, GRIDS and Virtualization, 2014

  12. C. Gu and Y. Gao. A Content-based Image Retrieval System Based on Hadoop and Lucene. Second International Conference on Cloud and Green Computing, 2012

  13. C. Sweeney, L. Liu, S. Arietta and J. Lawrence. HIPI: A Hadoop Image Processing Interface for Image-based MapReduce Tasks. Undergraduate Senior Thesis, School of Engineering and Applied Sciences, University of Virginia, 2011

  14. F. Long, H-J. Zhang and D.D. Fang. Fundamental of Content Based Image Retrieval chapter. In Multimedia information retrieval and management technological fundamentals and applications, 2003

  15. Wang, Y.: Evaluation of Scalable content-Based Image Retrieval Techniques. Computer Science and Engineering, The Chinese University of Hong Kong, Thesis submitted for the degree of Master of Philosophy (2007)

    Google Scholar 

  16. B. L. Deekshatulu. Learning Semantics in Content Based Image Retrieval (CBIR) – A brief review. Second Vaagdevi International Conference on Information Technology for Real World Problems, pp. 76-78, 2010

  17. Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. International Journal of Computer Vision 40(1), 25–47 (2000)

    Article  MATH  Google Scholar 

  18. Sun, J., Zheng, N.N., Tao, H., Shum, H.: Image hallucination with primal sketch priors. IEEE Conference on Computer Vision and Pattern Recognition 2, 729–736 (2003)

    Google Scholar 

  19. Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. IEEE Conference on Computer Vision and Pattern Classification 1, 275–282 (2004)

    Google Scholar 

  20. Xiong, Z., Sun, X., Wu, F.: Image hallucination with feature enhancement. IEEE Conference on Computer Vision and Pattern Classification 1, 2074–2081 (2009)

    Google Scholar 

  21. Donoho, D.L.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  22. Yang, J., Wright, J., Huang, T., Ma, Y.: Image supe rresolution via sparse representation. IEEE Trans. on Image Processing 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  23. R. Zeyde, M. Elad, and M. Protter, “On Single Image Scale-Up using Sparse-Representations,” Curves & Surfaces, Avignon France, June, 24-30, 2010

  24. J. Zhang, C. Zhao, S.W. Ma, D.B.Zhao.“Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation”.ISCAS, page 1688-1691.IEEE, (2012)

  25. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing over complete dictionaries for sparse representation. IEEE Trans. on Signal Processing 54(11), 4311–4322 (2006)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grant 2013AA014603, in part by the National Natural Science Foundation of China under Grant 61300202, and in part by the Natural Science Foundation of Shanghai under Grant 13ZR1452900.

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Correspondence to Lin Mei.

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This paper is the extended version (50 % new content) of the conference paper accepted by The 5th International Conference on Frontier Computing (FC2016).

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Xu, Z., Mei, L., Hu, C. et al. The big data analytics and applications of the surveillance system using video structured description technology. Cluster Comput 19, 1283–1292 (2016). https://doi.org/10.1007/s10586-016-0581-x

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  • DOI: https://doi.org/10.1007/s10586-016-0581-x

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