Skip to main content
SpringerLink
Log in

A novel efficient algorithm for duplicate video comparison in surveillance video storage systems

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Video surveillance system is playing an inevitable role in monitoring the world in uncountable and unimaginable aspects. There are numerous systems has been adopted to monitor. There are various application like forest monitoring system, border monitoring system, home security system, traffic monitoring in which the surveillance captured video data are stored for surveillance processing immediately or later. The stored video data are identical even for every minute, hour and this lead to need of more capacity of storage just for duplicate storage unintentionally. Some methods have been proposed to remove duplicate video in storage server. There must be good and efficient algorithm has to be offered. This work is proposed a novel duplicate video identification algorithm called Intelligent Duplicate Check Algorithm (IDCA) for removing duplicate video data without discarding any unique video data. The IDCA algorithm improvised duplicate identification considerable percentage and thus efficient duplicate free storage may be achieved.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Adimoolam M, Sugumaran M, Rajesh RS (2018a) A novel efficient redundancy free data communication model for intelligent surveillance system in WSN. J Adv Res Dyn Control Syst 10(3):743–754

    Google Scholar 

  • Adimoolam M, Sugumaran M, Rajesh RS (2018) A novel efficient clustering and secure data transmission model for spatiotemporal data in WSN, Int J Pure Appl Math 118(8):117–125. ISSN: 1311–8080.

  • A Large-scale Benchmark Dataset for Event Recognition in Surveillance Video" by Sangmin Oh, Anthony Hoogs, Amitha Perera, Naresh Cuntoor, Chia-Chih Chen, Jong Taek Lee, Saurajit Mukherjee, J.K. Aggarwal, Hyungtae Lee, Larry Davis, Eran Swears, Xiaoyang Wang, Qiang Ji, Kishore Reddy, Mubarak Shah, Carl Vondrick, Hamed Pirsiavash, Deva Ramanan, Jenny Yuen, Antonio Torralba, Bi Song, Anesco Fong, Amit Roy-Chowdhury, and Mita Desai. In: Proceedings of IEEE Comptuer Vision and Pattern Recognition (CVPR), 2011. [Online]. https://viratdata.org/.

  • Andy S, Haikal A Simple duplicate frame detection of MJPEG codec for video forensic. In: International conferences on information technology, information systems and electrical engineering, pp 321–324, 2017.

  • Boukhechba M, Bouzouane A, Bouchard B, Gaboury S, G-Vallerand C, Giroux S (2017) Prediction of next destinations from irregular Patterns, J. Ambient Intell Human Computing, Springer, pp. 1–25, 2017

  • Boulmakoul A, Karim L, Elbouziri A, Lbath A (2015) A system architecture for heterogeneous moving-object trajectory metamodel using generic sensors: tracking airport security case study. IEEE Syst J 9(1):1–9

    Article  Google Scholar 

  • Chen B, Shi L, Ke X (2019) A robust moving object detection in multi-scenario big data for video surveillance. IEEE Trans Circuits Syst Video Technol 29(4):982–995. https://doi.org/10.1109/TCSVT.2018.2828606

    Article  Google Scholar 

  • Chiu C-Y, Tsai T-H, Liou Y-C, Han G-W, Chan H-S (2014) Near-duplicate subsequence matching between the continuous stream and large video dataset. IEEE Trans Multimedia 16(7):1952–1962

    Article  Google Scholar 

  • Chou C-L, Chen H-T, Lee S-Y (2015) Pattern-based near-duplicate video retrieval and localization on web-scale videos. IEEE Trans Multimedia 17(3):382–395

    Article  Google Scholar 

  • Civelek M, Yazici A (2017) Automated moving object classification in wireless multimedia sensor networks. IEEE Sens J 17(4):1116–1131

    Article  Google Scholar 

  • Effective video surveillance frame rate-simple guide of IP camera bitrate setting, [Online]. https://www.unifore.net/ip-video-surveillance/simple-guide-of-ip-camera-bitrate-setting.html.

  • Fan C-T, Wang Y-K, Huang C-R (2017) Heterogeneous information fusion and visualization for a large-scale intelligent video surveillance system. IEEE Trans Syst Man Cybernet Syst 47(4):1–12

    Article  Google Scholar 

  • Huang Zi, Shen HT, Shao J, Cui B, Zhou X (2010) Practical online near-duplicate subsequence detection for continuous video streams. IEEE Trans Multimedia 12(5):386–398

    Article  Google Scholar 

  • Is a High Frame-rate Always a Must for Effective Video Surveillance?, [Online]. https://www.mistralsolutions.com/articles/17658-2.

  • Jamil N, Aziz A (2010) A unified approach to secure and robust hashing scheme for image and video authentication. In: IEEE 3rd international congress on image and signal processing, 1: 274–278.

  • IVY LAB Surveillance video dataset, [Online]. http://ivylab.kaist.ac.kr/demo/vs/dataset.htm.

  • Li Y, Hu L, Xia K et al (2019) Fast distributed video deduplication via locality-sensitive hashing with similarity ranking. J Image Video Process 51:1–11

    Google Scholar 

  • Liu W, Zhang M, Luo Z, Cai Y (2017) An Ensemble deep learning method for vehicle type classification on visual traffic surveillance sensors. IEEE Access 5:24417–24425

    Article  Google Scholar 

  • Meroni M, Atzberger C, Vancutsem C, Gobron N, Baret F, Lacaze R, Eerens H, Leo O (2013) Evaluation of agreement between space remote sensing SPOT-VEGETATION fAPAR time series. IEEE Trans Geosci Remote Sens 51(4):1951–1962

    Article  Google Scholar 

  • Pavlou KE, Snodgrass RT (2010) The tiled bitmap forensic analysis algorithm. IEEE Trans Knowl Data Eng 22(4):590–601

    Article  Google Scholar 

  • Rafferty J, Nugent C, Liu J, Chen L (2016) An approach to provide dynamic, illustrative, video-based guidance within a goal-driven smart home. J Ambient Intell Human Comput 8:1–12. https://doi.org/10.1007/s12652-016-0421-0

    Article  Google Scholar 

  • Sarkar A, Singh V, Ghosh P, Manjunath BS, Singh A (2010) Efficient and robust detection of duplicate videos in a large database. IEEE Trans Circuits Syst Video Technol 20(6):870–885

    Article  Google Scholar 

  • Scotti G, Marcenaro L, Coelho C, Selvaggi F, Regazzoni CS (2005) Dual camera intelligent sensor for high definition 360° surveillance. IEE Proc Vis Image Signal Process 152(2):250–257

    Article  Google Scholar 

  • Shaik GF, Chen M (2019) ViDupe-duplicate video detection as a service in cloud. In; IEEE Symposium on Integrated Network and Service Management (IM), Arlington, VA, USA, 2019, pp 725–726

  • Shao Z, Cai J, Wang Z (2018) Smart monitoring cameras driven intelligent processing to big surveillance video data. In: IEEE Transactions on Big Data, 4(1):105–116, doi: https://doi.org/10.1109/TBDATA.2017.2715815.

  • Signal Analysis for Machine Intelligence, [Online]. http://mklab.iti.gr/TC14/datasets.html.

  • Sun H, Shi W, Liang X, Yu Y (2020) VU: edge computing-enabled video usefulness detection and its application in large-scale video surveillance systems. IEEE Internet Things J 7(2):800–817. https://doi.org/10.1109/JIOT.2019.2936504

    Article  Google Scholar 

  • Toahchoodee M, Ray I, Anastasakis K, Georg G, Bordbar B (2009) Ensuring spatio-temporal access control for real-world applications. In: 14th ACM symposium on access control models and technologies, pp 13–22.

  • Vintrou E, Ienco D, Bégué A, Teisseire M (2013) Data mining, a promising tool for large-area cropland mapping. IEEE J Selected Topics Appl Earth Obs Remote Sens 6(5):2132–2138

    Article  Google Scholar 

  • Visor video surveillance online repository, [Online]. http://imagelab.ing.unimore.it/visor/video_categories.asp.

  • Viveros Martínez Y, López Domínguez E, Hernández Velázquez Y, Domínguez Isidro S, Medina Nieto MA, De La Calleja J (2019) Layered software architecture for the development of third-generation video surveillance systems. In: IEEE Access, 7:98507–98521. https://doi.org/10.1109/ACCESS.2019.2930401.

  • Wang H, Tian T, Ma M, Jun Wu (2017) Joint compression of near-duplicate videos. IEEE Trans Multimedia 19(5):908–920

    Article  Google Scholar 

  • Weitao Xu, Shen Y, Bergmann N, Wen Hu (2017) Sensor-assisted multi-view face recognition system on smart glass. IEEE Trans Mob Comput 17(1):197–210

    Google Scholar 

  • Yuan Y, Zheng H, Li Z, Zhang D (2010) Video action recognition with spatio-temporal graph embedding and spline modelling. In: IEEE international conference on acoustics speech and signal processing, pp 2422–2425.

  • Zhang C, Cao Q, Jiang H, Zhang W, Li J, Yao J (2020) A fast filtering mechanism to improve efficiency of large-scale video analytics. In: IEEE Trans Comput 69(6):914–928. doi: https://doi.org/10.1109/TC.2020.2970413.

  • Zhu J, Feng S, Yi D, Liao S, Lei Z, Li SZ (2015) High-performance video condensation system. IEEE Trans Circuits Syst Video Technol 25(7):1113–1124. https://doi.org/10.1109/TCSVT.2014.2363738

    Article  Google Scholar 

Download references

Acknowledgements

This work is for the research purpose and no funding is provided.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai, India

    N. M. Balamurugan

  2. Department of Computer Science and Engineering, Sridevi Women’s Engineering College, Hyderabad, Telangana, India

    T. K. S. Rathish babu

  3. School of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, India

    M. Adimoolam

  4. School of Computer Science and Engineering, Galgotias University, Greater Noida, India

    A. John

Authors
  1. N. M. Balamurugan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. K. S. Rathish babu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Adimoolam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. John
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. M. Balamurugan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balamurugan, N.M., Rathish babu, T.K.S., Adimoolam, M. et al. A novel efficient algorithm for duplicate video comparison in surveillance video storage systems. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03119-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12652-021-03119-7

Keywords

Search

Navigation