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Image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillance Videos

  • Raju Dhanakshirur RohanEmail author
  • Zeba ara Patel
  • Smita C. Yadavannavar
  • C. Sujata
  • Uma Mudengudi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1019)

Abstract

In this paper, we propose a geometric feature and frame segmentation based approach for video summarization. Video summarization aims to generate a summarized video with all the salient activities of the input video. We propose to retain the salient frames towards generation of video summary. We detect saliency in foreground and background of the image separately. We propose to model the image as MRF (Markov Random Field) and use MAP (Maximum a-posteriori) as final solution to segment the image into foreground and background. The salient frame is defined by the variation in feature descriptors using the geometric features. We propose to combine the probabilities of foreground and background segments being salient using DSCR (Dempster Shafer Combination Rule). We consider the summarized video as a combination of salient frames for a user defined time. We demonstrate the results using several videos in BL-7F dataset and compare the same with state of art techniques using retention ratio and condensation ratio as quality parameters.

Keywords

Video summarization Graph cut Geometric features Dempster Shafer Combination Rule (DSCR) 

References

  1. 1.
    Almeida, J., Torres, R.D.S., Leite, N.J.: Rapid video summarization on compressed video. In: 2010 IEEE International Symposium on Multimedia, pp. 113–120, December 2010Google Scholar
  2. 2.
    Bagheri, S., Zheng, J.Y.: Temporal mapping of surveillance video. In: 2014 22nd International Conference on Pattern Recognition, pp. 4128–4133, August 2014Google Scholar
  3. 3.
    Chan, W.K., Chang, J.Y., Chen, T.W., Tseng, Y.H., Chien, S.Y.: Efficient content analysis engine for visual surveillance network. IEEE Trans. Circuits Syst. Video Technol. 19(5), 693–703 (2009)CrossRefGoogle Scholar
  4. 4.
    Chang, W., Lee, S.Y.: Description of shape patterns using circular arcs for object detection. IET Comput. Vis. 7(2), 90–104 (2013)CrossRefGoogle Scholar
  5. 5.
    Chen, S.C., et al.: Target-driven video summarization in a camera network. In: 2013 IEEE International Conference on Image Processing, pp. 3577–3581, September 2013Google Scholar
  6. 6.
    Chia, A.Y.S., Rajan, D., Leung, M.K., Rahardja, S.: Object recognition by discriminative combinations of line segments, ellipses, and appearance features. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1758–1772 (2012)CrossRefGoogle Scholar
  7. 7.
    Cui, Y., Liu, W., Dong, S.: A time-slice optimization based weak feature association algorithm for video condensation. Multimedia Tools Appl. 75, 17515–17530 (2016)CrossRefGoogle Scholar
  8. 8.
    Kovesi, P.D.: MATLAB and Octave functions for computer vision and image processing, January 2000Google Scholar
  9. 9.
    Fan, C.T., Wang, Y.K., Huang, C.R.: Heterogeneous information fusion and visualization for a large-scale intelligent video surveillance system. IEEE Trans. Syst. Man Cybern. Syst. 47(4), 593–604 (2017)CrossRefGoogle Scholar
  10. 10.
    Fei, M., Jiang, W., Mao, W.: Memorable and rich video summarization. J. Vis. Commun. Image Represent. 42(C), 207–217 (2017)CrossRefGoogle Scholar
  11. 11.
    Hu, R.X., Jia, W., Ling, H., Zhao, Y., Gui, J.: Angular pattern and binary angular pattern for shape retrieval. IEEE Trans. Image Process. 23(3), 1118–1127 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Huang, C.R., Chung, P.C.J., Yang, D.K., Chen, H.C., Huang, G.J.: Maximum a posteriori probability estimation for online surveillance video synopsis. IEEE Trans. Circuits Syst. Video Technol. 24(8), 1417–1429 (2014)CrossRefGoogle Scholar
  13. 13.
    Li, X., Wang, Z., Lu, X.: Surveillance video synopsis via scaling down objects. IEEE Trans. Image Process. 25(2), 740–755 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Lu, Z., Grauman, K.: Story-driven summarization for egocentric video. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2714–2721, June 2013Google Scholar
  15. 15.
    Napoletano, P., Boccignone, G., Tisato, F.: Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy. IEEE Trans. Image Process. 24(11), 3266–3281 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Nie, Y., Sun, H., Li, P., Xiao, C., Ma, K.L.: Object movements synopsis viapart assembling and stitching. IEEE Trans. Visual Comput. Graph. 20(9), 1303–1315 (2014)CrossRefGoogle Scholar
  17. 17.
    Otani, M., Nakashima, Y., Sato, T., Yokoya, N.: Textual description-based video summarization for video blogs. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6, June 2015Google Scholar
  18. 18.
    Ou, S.H., Lee, C.H., Somayazulu, V.S., Chen, Y.K., Chien, S.Y.: On-line multi-view video summarization for wireless video sensor network. IEEE J. Sel. Top. Signal Process. 9(1), 165–179 (2015)CrossRefGoogle Scholar
  19. 19.
    Panda, R., Roy-Chowdhury, A.K.: Multi-view surveillance video summarization via joint embedding and sparse optimization. IEEE Trans. Multimedia 19(9), 2010–2021 (2017)CrossRefGoogle Scholar
  20. 20.
    Rochan, M., Ye, L., Wang, Y.: Video summarization using fully convolutional sequence networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 358–374. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01258-8_22CrossRefGoogle Scholar
  21. 21.
    Salehin, M.M., Paul, M.: Adaptive fusion of human visual sensitive features for surveillance video summarization. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 34(5), 814–826 (2017)CrossRefGoogle Scholar
  22. 22.
    Salehin, M., Zheng, L., Gao, J.: Conics detection method based on Pascal’s theorem. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015), pp. 491–497. INSTICC, SciTePress (2015)Google Scholar
  23. 23.
    Shih, H.C.: A novel attention-based key-frame determination method. IEEE Trans. Broadcast. 59(3), 556–562 (2013)CrossRefGoogle Scholar
  24. 24.
    Taj, M., Cavallaro, A.: Distributed and decentralized multicamera tracking. IEEE Signal Process. Mag. 28(3), 46–58 (2011)CrossRefGoogle Scholar
  25. 25.
    Valdés, V., Martínez, J.M.: On-line video summarization based on signature-based junk and redundancy filtering. In: 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services, pp. 88–91, May 2008Google Scholar
  26. 26.
    Valera, M., Velastin, S.A.: Intelligent distributed surveillance systems: a review. IEE Proc. - Vis. Image Signal Process. 152(2), 192–204 (2005)CrossRefGoogle Scholar
  27. 27.
    Zhang, S., Roy-Chowdhury, A.K.: Video summarization through change detection in a non-overlapping camera network. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3832–3836, September 2015Google Scholar
  28. 28.
    Zhao, B., Li, X., Lu, X.: Hierarchical recurrent neural network for video summarization. In: ACM Multimedia (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Raju Dhanakshirur Rohan
    • 1
    Email author
  • Zeba ara Patel
    • 1
  • Smita C. Yadavannavar
    • 1
  • C. Sujata
    • 1
  • Uma Mudengudi
    • 1
  1. 1.KLE Technological UniversityHubliIndia

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