Skip to main content
SpringerLink
Log in

A Comprehensive Investigation About Video Synopsis Methodology and Research Challenges

  • Conference paper
  • First Online:
Inventive Computation and Information Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 173))

  • 940 Accesses

Abstract

With enormous growth in video surveillance technology, the challenges in terms of data retrieval, monitoring, and browsing have been increased. A smarter solution for this is a video synopsis technique that represents prolonged video in a compact form based on the object trajectories rather than the key frame approach. It converts long video footage into shorter video form while preserving all the activities of the original video. The object trajectories are shifted in time domain as well as in spatial domain to offer the maximum compactness while maintaining the sequence of original source video. This paper gives the brief about the different approaches, evaluation parameters, and datasets used to assess the quality of synopsis video. The main objective is to investigate the query-based video synopsis useful for data retrieval through activity clustering steps in the synopsis framework that will also help to solve societal problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Markets and Markets (2019) Video surveillance market survey. https://www.marketsandmarkets.com/Market-Reports/video-surveillance-market-645.html

  2. Tewari D (2020) Video surveillance market by system type and application (commercial, military & defense, infrastructure, residential, and others): global opportunity analysis and industry forecast, 2017–2025. Video surveillance Market Outlook 2027.

    Google Scholar 

  3. Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimedia Comput Commun Appl 3(1):3–es

    Google Scholar 

  4. Money AG, Agius H (2008) Video summarisation: A conceptual framework and survey of the state of the art. J Vis Commun Image Represent 19(2):121–143

    Article  Google Scholar 

  5. Smith MA (1995) Video skimming for quick browsing based on audio and image characterization

    Google Scholar 

  6. Petrovic N, Jojic N, Huang TS (2005) Adaptive video fast forward. Multimedia Tools Appl 26(3):327–344

    Article  Google Scholar 

  7. Mahapatra A et al (2016) MVS: a multi-view video synopsis framework. Signal Processi: Image Commun 42:31–44

    Google Scholar 

  8. Feng S et al (2012) Online content-aware video condensation. 2082–2087

    Google Scholar 

  9. Ahmed SA et al (2019) Query-based video synopsis for intelligent traffic monitoring applications. IEEE Trans Intell Transport Syst 1–12.

    Google Scholar 

  10. Ruan T et al (2019) Rearranging online tubes for streaming video synopsis: a dynamic graph coloring approach. IEEE Trans Image Process 28(8):3873–3884

    Article  MathSciNet  Google Scholar 

  11. Ghatak S et al (2020) An improved surveillance video synopsis framework: a HSATLBO optimization approach. Multimedia Tools Appl 79(7):4429–4461

    Article  Google Scholar 

  12. Li X, Wang Z, Lu X (2018) Video synopsis in complex situations. IEEE Trans Image Process 27(8):3798–3812

    Article  MathSciNet  Google Scholar 

  13. Ra M, Kim W (2018) Parallelized tube rearrangement algorithm for online video synopsis. IEEE Signal Process Lett 25(8):1186–1190

    Article  Google Scholar 

  14. He Y et al (2017) Fast online video synopsis based on potential collision graph. IEEE Signal Process Lett 24(1):22–26

    Article  Google Scholar 

  15. Tian Y et al (2016) Surveillance video synopsis generation method via keeping important relationship among objects. IET Comput Vis 10:868–872

    Article  Google Scholar 

  16. Balasubramanian Y, Sivasankaran K, Krishraj SP (2016) Forensic video solution using facial feature-based synoptic video footage record. IET Comput Vis 10(4):315–320

    Article  Google Scholar 

  17. Zhong R et al (2014) Fast synopsis for moving objects using compressed video. IEEE Signal Process Lett 21:1–1

    Google Scholar 

  18. Zhu J et al (2015) High-performance video condensation system. IEEE Trans Circuits Syst Video Technol 25(7):1113–1124

    Article  Google Scholar 

  19. Pritch Y et al (2009) Clustered Synopsis of Surveillance Video. In: 2009 Sixth IEEE international conference on advanced video and signal based surveillance

    Google Scholar 

  20. Wang S, Wang Z-Y, Hu R-M (2013) Surveillance video synopsis in the compressed domain for fast video browsing. J Vis Commun Image Represent 24:1431–1442

    Article  Google Scholar 

  21. Li X, Wang Z, Lu X (2016) Surveillance video synopsis via scaling down objects. IEEE Trans Image Process 25(2):740–755

    Article  MathSciNet  Google Scholar 

  22. Huang C-R et al (2014) Maximum a Posteriori probability estimation for online surveillance video synopsis. IEEE Trans Circ Syst Video Technol 24:1417–1429

    Article  Google Scholar 

  23. Pappalardo G et al (2019) A new framework for studying tubes rearrangement strategies in surveillance video synopsis. In: 2019 IEEE international conference on image processing (ICIP)

    Google Scholar 

  24. Redmon J, Farhadi A (2017) YOLO9000: Better, faster, stronger. IEEE Conf Comput Vis Pattern Recogn (CVPR) 2017:6517–6525

    Google Scholar 

  25. Chien-Li C et al (2015) Coherent event-based surveillance video synopsis using trajectory clustering. . In: 2015 IEEE international conference on multimedia & expo workshops (ICMEW)

    Google Scholar 

  26. Lin W et al (2015) Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis. Neurocomputing 155:84–98

    Article  Google Scholar 

  27. Rav-Acha A, Pritch Y, Peleg S (2006) Making a long video short: dynamic video synopsis. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 1, pp 435–441

    Google Scholar 

  28. He Y et al (2016) Graph coloring based surveillance video synopsis. Neurocomputing 225

    Google Scholar 

  29. Nie Y et al (2019) Collision-free video synopsis incorporating object speed and size changes. IEEE Trans Image Process: Publ IEEE Signal Process Soc

    Google Scholar 

  30. Li K et al (2016) An effective video synopsis approach with seam carving. IEEE Signal Process Lett 23(1):11–14

    Article  Google Scholar 

  31. Fu W et al (2014) Online video synopsis of structured motion. Neurocomputing 135:155–162

    Article  Google Scholar 

  32. Namitha K, Narayanan A (2018) Video synopsis: state-of-the-art and research challenges

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Pune, India

    Swati Jagtap & Nilkanth B. Chopade

Authors
  1. Swati Jagtap
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nilkanth B. Chopade
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swati Jagtap .

Editor information

Editors and Affiliations

  1. RVS Technical Campus, Coimbatore, India

    S. Smys

  2. “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Computer Science Department, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

  4. Czech Technical University, Praha, Czech Republic

    Pavel Lafata

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jagtap, S., Chopade, N.B. (2021). A Comprehensive Investigation About Video Synopsis Methodology and Research Challenges. In: Smys, S., Balas, V.E., Kamel, K.A., Lafata, P. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 173. Springer, Singapore. https://doi.org/10.1007/978-981-33-4305-4_66

Download citation

Publish with us

Policies and ethics

Search

Navigation