Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Semi-supervised vision-based maritime surveillance system using fused visual attention maps

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents a vision-based system for maritime surveillance, using moving PTZ cameras. The proposed methodology fuses a visual attention method that exploits low-level image features appropriately selected for maritime environment, with appropriate tracker, without making any assumptions about environmental or visual conditions. The offline initialization is based on large graph semi-supervised technique. System’s performance was evaluated with videos from cameras placed at Limassol port and Venetian port of Chania. Results suggest high detection ability, despite dynamically changing visual conditions and different kinds of vessels, all in real time.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.poseidonproject.gr/

  2. https://github.com/konstmakantasis/Poseidon

References

  1. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: IEEE Conference on computer vision and pattern recognition, 2009. CVPR 2009, pp 1597–1604

  2. Achanta R, Susstrunk S (2010) Saliency detection using maximum symmetric surround. In: 2010 17th IEEE International conference on image processing (ICIP), pp 2653–2656

  3. Agnew DJ, Pearce J, Pramod G, Peatman T, Watson R, Beddington JR, Pitcher TJ (2009) Estimating the worldwide extent of illegal fishing. PLoS ONE 4(2)

  4. Albrecht T, Tan T, West GAW, Ly T, Moncrieff S (2011) Vision-based attention in maritime environments. In: Communications and signal processing (ICICS) 2011 8th international conference on information, pp 1–5

  5. Albrecht T, West GAW, Tan T, Ly T (2010) Multiple views tracking of maritime targets. In: 2010 international conference on digital image computing: techniques and applications (DICTA), pp 302– 307

  6. Albrecht T, West GAW, Tan T, Ly T (2011) Visual maritime attention using multiple low-level features and na #x0ef;ve bayes classification. In: 2011 International conference on digital image computing techniques and applications (DICTA), pp 243–249

  7. Alexe B, Deselaers T, Ferrari V (2010) What is an object? In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 73–80

  8. Auslander B, Gupta KM, Aha DW (2011) A comparative evaluation of anomaly detection algorithms for maritime video surveillance, vol 8019, pp 801907–801907–14

  9. Belkin M, Niyogi P (2002) Using manifold structure for partially labelled classification, p 929

  10. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  11. Doulamis N, Doulamis A (2012) Fast and adaptive deep fusion learning for detecting visual objects. In: Fusiello A, Murino V, Cucchiara R (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations, number 7585 in Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 345–354

    Google Scholar 

  12. Fischer Y, Bauer A (2010) Object-oriented sensor data fusion for wide maritime surveillance. In: 2010 International waterside security conference (WSS), pp 1–6

  13. Kaimakis P, Tsapatsoulis N (2013) Background modeling methods for visual detection of maritime targets. In: Proceedings of the 4th ACM/IEEE international workshop on analysis and retrieval of tracked events and motion in imagery stream, ARTEMIS ’13, pp 67–76. ACM, New York

  14. Lei P-R (2013) Exploring trajectory behavior model for anomaly detection in maritime moving objects. In: 2013 IEEE International Conference on Intelligence and Security Informatics (ISI), pp 271– 271

  15. Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum H-Y (2011) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33 (2):353–367

    Article  Google Scholar 

  16. Liu W, He J, Chang S-F (2010) Large graph construction for scalable semi-supervised learning. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 679– 686

  17. Makantasis K, Doulamis A, Doulamis N (2013) Vision-based maritime surveillance system using fused visual attention maps and online adaptable tracker. In: 2013 14th international workshop on image analysis for multimedia interactive services (WIAMIS), pp 1–4

  18. Makantasis K, Doulamis A, Matsatsinis NF (2012) Student-t background modeling for persons’ fall detection through visual cues. In: 2012 13th International workshop on image analysis for multimedia interactive services (WIAMIS), pp 1–4

  19. Maresca S, Greco M, Gini F, Grasso R, Coraluppi S, Horstmann J (2010) Vessel detection and classification: An integrated maritime surveillance system in the tyrrhenian sea. In: 2010 2nd International Workshop on Cognitive Information Processing (CIP), pp 40–45

  20. McIlhagga W (2011) The canny edge detector revisited. Int J Comput Vis 91(3):251–261

    Article  MathSciNet  MATH  Google Scholar 

  21. Nadler B, Srebro N, Zhou X (2009) Statistical analysis of semi-supervised learning: The limit of infinite unlabelled data. In: Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A (eds) Advances in neural information processing systems, vol 22. Curran Associates, Inc., pp 1330– 1338

  22. Nilsson M, van Laere J, Ziemke T, Edlund J (2008) Extracting rules from expert operators to support situation awareness in maritime surveillance. In: 2008 11th International conference on information fusion, pp 1–8

  23. Onuoha F (2009) Sea piracy and maritime security in the Horn of Africa: The Somali coast and Gulf of Aden in perspective. Afr Secur Rev 18(3):31–44

    Article  Google Scholar 

  24. Rodriguez Sullivan MD, Shah M (2008) Visual surveillance in maritime port facilities, vol 6978, pp 697811–697811–8

  25. Socek D, Culibrk D, Marques O, Kalva H, Furht B (2005) A hybrid color-based foreground object detection method for automated marine surveillance. In: Blanc-Talon J, Philips W, Popescu D, Scheunders P (eds) Advanced Concepts for Intelligent Vision Systems, number 3708 in Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 340– 347

    Google Scholar 

  26. Stanslas PT (2010) Transborder human trafficking in malaysian waters: addressing the root causes. J Mar L Com 41:595

    Google Scholar 

  27. Szpak ZL, Tapamo JR (2011) Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set. Expert Syst Appl 38(6):6669–6680

    Article  Google Scholar 

  28. Vandecasteele A, Devillers R, Napoli A (2013) A semi-supervised learning framework based on spatio-temporal semantic events for maritime anomaly detection and behavior analysis. In: Proceedings CoastGIS 2013 conference: monitoring and adapting to change on the coast

  29. Voles P (1999) Target identification in a complex maritime scene, vol 1999. IEE, pp 15–15

  30. Wijnhoven R, van Rens K, Jaspers E, de With P (2010) Online learning for ship detection in maritime surveillance, pp 73–80

  31. Yasri I, Hamid NH, Yap VV (2008) Performance analysis of FPGA based sobel edge detection operator. In: International conference on electronic design, 2008. ICED 2008, pp 1–4

  32. Zemmari R, Daun M, Feldmann M, Nickel U (2013) Maritime surveillance with GSM passive radar: Detection and tracking of small agile targets. In: Radar Symposium (IRS), 2013 14th International, vol 1, pp 245–251

  33. Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International conference on pattern recognition, 2004. ICPR 2004, vol 2, pp 28–31

  34. Zhu X (2003) Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International conference on machine learning (ICML-2003), vol 20, p 912

Download references

Acknowledgments

The work has been partially supported by IKY Fellowships of excellence for postgraduate studies in Greece–Siemens program. The work has, also, been supported by European Union funds and national funds from Greece and Cyprus under the project POSEIDON: Development of an Intelligent System for Coast Monitoring using Camera Arrays and Sensor Networks in the context of the inter-regional programme INTERREG (Greece-Cyprus cooperation) - contract agreement K1 3 1017/6/2011.

Author information

Authors and Affiliations

  1. Technical University of Crete, Chania, Greece

    Konstantinos Makantasis, Eftychios Protopapadakis & Nikolaos Matsatsinis

  2. National Technical University of Athens, Athens, Greece

    Anastasios Doulamis

Authors
  1. Konstantinos Makantasis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eftychios Protopapadakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anastasios Doulamis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nikolaos Matsatsinis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eftychios Protopapadakis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Makantasis, K., Protopapadakis, E., Doulamis, A. et al. Semi-supervised vision-based maritime surveillance system using fused visual attention maps. Multimed Tools Appl 75, 15051–15078 (2016). https://doi.org/10.1007/s11042-015-2512-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2512-x

Keywords

Search

Navigation