An Improved Algorithm for Video Abstract

  • Jianlei Zhang
  • Qin LiEmail author
  • Wenfeng Shen
  • Shengbo Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 699)


In this paper, we study the foreground object extraction, the trajectory extraction, the trajectory combination optimization and other key technologies of the surveillance video abstract generation technology. Put forward a kind of improved algorithm, through the pre-processing based on Focus Stacking, foreground object extraction of the foreground object shadow removal, the trajectory synthesis optimization. As a result, the foreground extraction accuracy rate was increased by 2.78%; because of shadow removal of the foreground object, the collision rate of the foreground object in the synthesized video is reduced by 15.77%; The use of Semi-Transparent Handling Collision (STHC) makes the trajectory of the foreground object is not interrupted, the video frame information is not lose and the compression rate is increased by about 10%. The algorithm is applied in this paper, and the optimization effect is observed through the whole system test. As a result, the clarity of the synthesized video is increased, the integrity of the video’s information is enhanced, and the compression rate of the video is improved.


Video synopsis Focus Stacking Shadow remove Trajectory synthesis 



This work is funded by “Peak disciplines achievements in 2015 of the School of Film and Television Art Technology of Shanghai University” and “Shanghai University Material Genetic Engineering Institute” (No. 14DZ2261200). Thanks for the support of the high performance computing center.


  1. 1.
    Huang, K., Chen, X., Kang, Y., Tan, T.: A survey of intelligent video surveillance technology. J. Comput. Sci. 6, 1093–1118 (2015)Google Scholar
  2. 2.
    Kang, M.: Research of video abstract algorithm based object. Ph.D. dissertation, Xi’an Electronic and Science University (2014)Google Scholar
  3. 3.
    Wang, J., Jiang, X., Sun, T.: Summary of video synopsis technology. Chin. J. Image Graph. 19(12), 1940–1943 (2014)Google Scholar
  4. 4.
    Olivier, B., Marc, V.D.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011). A Publication of the IEEE Signal Processing SocietyMathSciNetCrossRefGoogle Scholar
  5. 5.
    Wu, Q., Shi, P.: Analysis of video abstract technology. J. Commun. Univ. China Nat. Sci. Edn. 15(2), 54–58 (2008)Google Scholar
  6. 6.
    Bhaumik, H., Bhattacharyya, S., Dutta, S., Chakraborty, S.: Towards redundancy reduction in storyboard representation for static video summarization. In: International Conference on Advances in Computing, Communications and Informatics, pp. S56–S57 (2014)Google Scholar
  7. 7.
    Zhu, X., Wu, X., Fan, J., Elmagarmid, A.K., Aref, W.G.: Exploring video content structure for hierarchical summarization. Multimedia Syst. 10(2), 98–115 (2004)CrossRefGoogle Scholar
  8. 8.
    Yeh, C.H., Kuo, C.H., Liou, R.W.: Movie story intensity representation through audiovisual tempo analysis. Multimedia Tools Appl. 44(2), 205–228 (2009)CrossRefGoogle Scholar
  9. 9.
    Zhang, S.H., Li, X.Y., Hu, S.M., Martin, R.R.: Online video stream abstraction and stylization. IEEE Trans. Multimedia 13(6), 1286–1294 (2010)CrossRefGoogle Scholar
  10. 10.
    Winnemöller, H., Olsen, S.C., Gooch, B.: Real-time video abstraction. ACM Trans. Graph. 25(3), 1221–1226 (2006)CrossRefGoogle Scholar
  11. 11.
    Zhang, L., Cao, Y., Ding, G., Yong, W.: A computable visual attention model for video skimming. In: IEEE International Symposium on Multimedia, pp. 667–672 (2008)Google Scholar
  12. 12.
    Rav-Acha, A., Pritch, Y., Peleg, S.: Making a long video short: dynamic video synopsis. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 435–441 (2006)Google Scholar
  13. 13.
    Pritch, Y., Ratovitch, S., Hendel, A., Peleg, S.: Clustered synopsis of surveillance video. In: 6th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 195–200 (2009)Google Scholar
  14. 14.
    Pritch, Y., Rav-Acha, A., Gutman, A., Peleg, S.: Webcam synopsis: peeking around the world. In: ICCV 2007, Riode Janiero, pp. 1–8 (2007)Google Scholar
  15. 15.
    Vural, U., Akgul, Y.S.: Eye-gaze based real-time surveillance video synopsis. Pattern Recogn. Lett. 30(12), 1151–1159 (2009)CrossRefGoogle Scholar
  16. 16.
    Li, T., Mei, T., Kweon, I.S., Hua, X.S.: Video^M: multi-video synopsis. In: IEEE International Conference on Data Mining Workshops, pp. 854–861 (2008)Google Scholar
  17. 17.
    Li, T., Mei, T., Kweon, I.S., et al.: Multi-video synopsis for video representation. Sig. Process. 89(12), 2354–2366 (2009)CrossRefzbMATHGoogle Scholar
  18. 18.
    Qian, Q., Gunturk, B.K.: Extending depth of field and dynamic range from differently focused and exposed images. Multidimens. Syst. Sig. Process. 27, 1–17 (2015)Google Scholar
  19. 19.
    Qian, Q., Gunturk, B.K., Batur, A.U.: Joint focus stacking and high dynamic range imaging. In: Proceedings of SPIE, pp. 866 004–866 004–7 (2013)Google Scholar
  20. 20.
    Dezeeuw, P., Gledhill, L., Cardwell, M.W.: Motor controlled macro rail for close-up focus-stacking photography (2012)Google Scholar
  21. 21.
    Brecko, J., Mathys, A., Dekoninck, W., Leponce, M., Vandenspiegel, D., Semal, P.: Focus stacking: comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. Zookeys 464, 1–23 (2014)CrossRefGoogle Scholar
  22. 22.
    Zhang, C., Bastian, J., Shen, C., Van den Hengel, A., Shen, T.: Extended depth-of-field via focus stacking and graph cuts. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 1272–1276 (2013)Google Scholar
  23. 23.
    Demandolx, D., Ricard, D.A., Dideriksen, T.L., Chiu, K.G.: Combining multiple images in bracketed photography (2013)Google Scholar
  24. 24.
    Sanin, A., Sanderson, C., Lovell, B.C.: Shadow detection: a survey and comparative evaluation of recent methods. Pattern Recogn. 45(4), 1684–1695 (2012)CrossRefGoogle Scholar
  25. 25.
    Jiang, D.: Research on video abstract generation based on clustering mining. Ph.D. dissertation, Zhejiang University (2010)Google Scholar
  26. 26.
    Sun, L., Xing, J., Ai, H., Lao, S.: A tracking based fast online complete video synopsis approach. In: International Conference on Pattern Recognition, pp. 1956–1959 (2012)Google Scholar
  27. 27.
    Xu, L., Liu, H., Yan, X., Liao, S., Zhang, X.: Optimization method for trajectory combination in surveillance video synopsis based on genetic algorithm. J. Ambient Intell. Hum. Comput. 6(5), 1–11 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Jianlei Zhang
    • 1
  • Qin Li
    • 1
    Email author
  • Wenfeng Shen
    • 1
  • Shengbo Chen
    • 1
  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiPeople’s Republic of China

Personalised recommendations