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Dynamic Object Indexing Technique for Distortionless Video Synopsis

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Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB) (ISMAC 2018)

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 30))

Abstract

With a development of observation cameras, the measure of caught recordings extends. Physically dissecting and recovering reconnaissance video is work concentrated and costly. It is substantially more important to create a video description and the video can be observed in a good manner. So, here we describe a novel video outline way to deal with produce consolidated video, which utilizes a protest following technique for extracting imperative items. This strategy will create video objects and a crease cutting technique to gather the first video. Finally, output results that our proposed strategy can accomplish a high buildup rate while safeguarding all the imperative objects of intrigue. Hence, in this method, we can empower clients to see the synopsis video with high impact.

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References

  1. Rubinstein M, Shamir A, Avidan S (2008) Improved seam carving for video retargeting. ACM Trans Graph 27(3):16. ACM

    Google Scholar 

  2. Zhong R, Hu R, Wang Z, Wang S (2014) Fast synopsis for moving objects using 925 compressed video. Signal Process Lett IEEE 21(7):834–838

    Article  Google Scholar 

  3. Stauffer C, Grison WEL (1999) Adaptive background mixture models for real-time tracking. In: CVPR, pp 246–252

    Google Scholar 

  4. Boult TE, Micheals RJ, Gao X, Eckmann M (2001) Into the woods: visual surveillance of non-cooperative and camouflaged targets in complex outdoor settings. Proc IEEE 89:1382–1401

    Article  Google Scholar 

  5. Zhang Z, Huang K, Tan T (2008) Multi-thread parsing for recognizing complex events in videos. In: Torr P, Zisserman A (eds) 10th ECCV, Part III, pp 738–751

    Google Scholar 

  6. Zeng W, Du J, Gao W, Huang Q (2005) Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model. Real-Time Imaging 11:290–299

    Article  Google Scholar 

  7. O’callaghan D, Lew EL (1995) Method and apparatus for video on demand with fast forward, reverse and channel pause, US Patent 5,477,263, 19 Dec 1995

    Google Scholar 

  8. Gandhi NM, Misra R (2015) Performance comparison of parallel graph coloring algorithms on bsp model using hadoop. In: International conference on computing, networking and communications (ICNC). IEEE, pp 110–116

    Google Scholar 

  9. Zhong R, Hu R, Wang Z, Wang S (2014) Fast synopsis for moving objects using compressed video. IEEE Signal Process Lett 21(7):834–838

    Article  Google Scholar 

  10. Li Z, Ishwar P, Konrad J (2009) Video condensation by ribbon carving. IEEE Trans Image Process 18(11):2572–2583

    Article  MathSciNet  MATH  Google Scholar 

  11. Yoo JW, Yea S, Park IK (2013) Content-driven retargeting of stereoscopic images. IEEE Signal Process Lett 20(5):519–522

    Article  Google Scholar 

  12. Panagiotakis C, Ovsepian N, Michael E (2013) Video synopsis based on a sequential distortion minimization method. In: International conference on computer analysis of images and patterns

    Google Scholar 

  13. Ye Y, Yi-jun L, Yan-qing W (2014) An improved aco algorithm for the bin packing problem with conflicts based on graph coloring model. In: International conference on management science & engineering (ICMSE). IEEE, pp 3–9

    Google Scholar 

  14. Pritch Y, Rav-Acha A, Peleg S (2008) Nonchronological video synopsis and indexing. IEEE Trans Patt Anal Mach Intell 30(11):1971–1984

    Article  Google Scholar 

  15. Javed O, Shafique K, Shah M (2007) Automated visual surveillance in realistic scenarios. IEEE Multimedia 14:30–39

    Article  Google Scholar 

  16. Babu RV, Ramakrishnan KR, Srinivasan SH (2004) Video object segmentation: a compressed domain approach. CSVT 14:462–474

    Google Scholar 

  17. Oh J, Wen Q, Hwang S, Lee J (2004) Video abstraction, video data management and information retrieval, pp 321–346

    Google Scholar 

  18. Yeung MM, Yeo B-L (1997) Video visualization for compact presentation and 855 fast browsing of pictorial content. Circuits Syst Video Technol IEEE Trans 7(5):771–785

    Article  Google Scholar 

  19. Huang C-R, Chung P-CJ, Yang D-K, Chen H-C, Huang G-J (2014) Maximum a posteriori probability estimation for online surveillance video synopsis. Circuits Syst Video Technol IEEE Trans 24(8):1417–1429

    Article  Google Scholar 

  20. Feng S, Lei Z, Yi D, Li SZ (2012) Online content-aware video condensation. In: IEEE Conference 930 on computer vision and pattern recognition (CVPR). IEEE, pp 2082–2087

    Google Scholar 

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Acknowledgements

We need to thank the accommodating remarks and recommendations from the unknown analysts. The proposed algorithm is developed by me and images which are utilized in this work are taken with the help of CCTV cameras, except office, and snooker videos. These are downloaded from the Google and open source data set.

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Authors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, Tamil Nadu, India

    G. Thirumalaiah & S. Immanuel Alex Pandian

Authors
  1. G. Thirumalaiah
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  2. S. Immanuel Alex Pandian
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Corresponding author

Correspondence to G. Thirumalaiah .

Editor information

Editors and Affiliations

  1. SCAD Institute of Technology, Palladam, India

    Durai Pandian

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. School of Computer and Security Science, Edith Cowan University, Joondalup, WA, Australia

    Zubair Baig

  4. Wenzhou Medical University, Wenzhou, China

    Fuqian Shi

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Thirumalaiah, G., Immanuel Alex Pandian, S. (2019). Dynamic Object Indexing Technique for Distortionless Video Synopsis. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_85

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  • DOI: https://doi.org/10.1007/978-3-030-00665-5_85

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00664-8

  • Online ISBN: 978-3-030-00665-5

  • eBook Packages: EngineeringEngineering (R0)

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