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Background modeling methods for visual detection of maritime targets

Published: 21 October 2013 Publication History

Abstract

We propose a system for real-time detection of maritime targets based on monocular video data. In the absence of a priori knowledge about their appearance, targets are detected implicitly via the statistical modeling of the scene's nonstationary background. A probabilistic treatment regarding target compactness is also presented. The proposed system currently acts as a stand-alone maritime surveillance application, and may also be used as an early detection stage within a larger maritime target tracking framework.

References

[1]
A. K. Bachoo, F. le Roux, and F. Nicolls. An optical tracker for the maritime environment. In Proceedings of SPIE on Signal Processing, Sensor Fusion, and Target Recognition, volume 8050, Orlando, Florida, USA, 4 May 2011.
[2]
Y. Bar-Shalom, X. R. Li, and T. Kirubarajan. Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software. Wiley, 1st edition, June 2001.
[3]
C. Bibby and I. Reid. Visual tracking at sea. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 1841--1846, Barcelona, Spain, 18--22 May 2005.
[4]
D. D. Bloisi and L. Iocchi. ARGOS -- a video surveillance system for boat traffic monitoring in Venice. International Journal of Pattern Recognition and Artificial Intelligence, 23(7):1477--1502, 2009.
[5]
G. R. Bradski. Real time face and object tracking as a component of a perceptual user interface. In Proceedings of the 4th IEEE Workshop on Applications of Computer Vision, pages 214--219, Princeton, New Jersey, USA, 19--21 October 1998.
[6]
G. R. Bradski and A. Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, 1st edition, September 2008.
[7]
A. Colombari, A. Fusiello, and V. Murino. Video objects segmentation by robust background modeling. In Proceedings of the 14th IEEE International Conference on Image Analysis and Processing, pages 155--164, Modena, Italy, 10--14 September 2007.
[8]
D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603--619, May 2002.
[9]
R. Cucchiara, C. Grana, M. Piccardi, and A. Prati. Detecting objects, shadows and ghosts in video streams by exploiting color and motion information. In Proceedings of the 11th IEEE International Conference on Image Analysis and Processing, pages 360--365, Palermo, Italy, 26--28 September 2001.
[10]
A. Elgammal, R. Duraiswami, and L. S. Davis. Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(11):1499--1504, November 2003.
[11]
T. Horprasert, D. Harwood, and L. S. Davis. A statistical approach for real-time robust background subtraction and shadow detection. In Proceedings of the 7th IEEE International Conference on Computer Vision FRAME-RATE Workshop, Kerkyra, Greece, 21 September 1999.
[12]
S. Jabri, Z. Duric, H. Wechsler, and A. Rosenfeld. Detection and location of people in video images using adaptive fusion of color and edge information. In Proceedings of the 15th IEEE International Conference on Pattern Recognition, volume 4, pages 627--630, Barcelona, Spain, 3--7 September 2000.
[13]
P. KaewTraKulPong and R. Bowden. An improved adaptive background mixture model for realtime tracking with shadow detection. In Proceedings of the 2nd European Workshop on Advanced Video Based Surveillance Systems, Kingston upon Thames, UK, September 2001.
[14]
P. Kaimakis, S. I. Hill, W. J. Fitzgerald, and J. Bacon. 3D multi-car tracking based on monocular video. In Proceedings of the 2nd IEEE International Workshop on Cognitive Information Processing, pages 203--208, Elba Island, Italy, 14--6 June 2010.
[15]
X. Mei and H. Ling. Robust visual tracking and vehicle classification via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11):2259--2272, November 2011.
[16]
S. Messelodi, C. M. Modena, and M. Zanin. A computer vision system for the detection and classification of vehicles at urban road intersections. Transactions on Pattern Analysis and Applications, 8(1):17--31, September 2005.
[17]
F. E. Nathanson, J. P. Reilly, and M. N. Coneh. Radar Design Principles. SciTech Publishing, 2nd edition, January 1999.
[18]
W. Ng, J. Li, S. J. Godsill, and J. Vermaak. A review of recent results in multiple target tracking. In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, pages 40--45, Zagreb, Croatia, 15--17 September 2005.
[19]
P. Pérez, C. Hue, J. Vermaak, and M. Gangnet. Color-based probabilistic tracking. In Proceedings of the 7th European Conference on Computer Vision, volume 1, pages 661--675, Copenhagen, Denmark, 27 May-2 June 2002.
[20]
D. Ramanan and D. A. Forsyth. Using temporal coherence to build models of animals. In Proceedings of the 9th IEEE International Conference on Computer Vision, volume 1, pages 338--345, Nice, France, 13--16 October 2003.
[21]
M. I. Skolnik. Introduction to Radar Systems. McGraw-Hill, 3rd edition, December 2002.
[22]
C. Stauffer and W. E. L. Grimson. Adaptive background mixture models for real-time tracking. In Proceedings of the 18th IEEE International Conference on Computer Vision and Pattern Recognition, volume 2, pages 246--252, Fort Collins, Colorado, USA, 23--25 June 1999.
[23]
C. Tomasi and R. Manduchi. Bilateral filtering for gray and color images. In Proceedings of the 6th IEEE International Conference on Computer Vision, pages 839--846, Bombay, India, 4--7 Jan 1998.
[24]
Z. Zivkovic. Improved adaptive Gaussian mixture model for background subtraction. In Proceedings of the 17th IEEE International Conference on Pattern Recognition, volume 2, pages 28--31, Cambridge, UK, 23--26 August 2004.

Cited By

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  • (2024)Maritime Vessel Detection and Classification in Harbor Environment Using Deep LearningDigital Interaction and Machine Intelligence10.1007/978-3-031-66594-3_1(3-17)Online publication date: 15-Aug-2024
  • (2020)Background segmentation in multicolored illumination environmentsThe Visual Computer10.1007/s00371-020-01981-8Online publication date: 6-Oct-2020
  • (2016)Soccer player tracking using particle filters2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)10.1109/ISSPIT.2016.7886009(57-62)Online publication date: Dec-2016
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cover image ACM Conferences
ARTEMIS '13: Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
October 2013
94 pages
ISBN:9781450323932
DOI:10.1145/2510650
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 21 October 2013

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Author Tags

  1. background modeling
  2. event-based systems
  3. maritime target detection

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MM '13
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MM '13: ACM Multimedia Conference
October 21, 2013
Barcelona, Spain

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Cited By

View all
  • (2024)Maritime Vessel Detection and Classification in Harbor Environment Using Deep LearningDigital Interaction and Machine Intelligence10.1007/978-3-031-66594-3_1(3-17)Online publication date: 15-Aug-2024
  • (2020)Background segmentation in multicolored illumination environmentsThe Visual Computer10.1007/s00371-020-01981-8Online publication date: 6-Oct-2020
  • (2016)Soccer player tracking using particle filters2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)10.1109/ISSPIT.2016.7886009(57-62)Online publication date: Dec-2016
  • (2016)Saliency-Based Detection for Maritime Object Tracking2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW.2016.159(1257-1264)Online publication date: Jun-2016
  • (2016)Semi-supervised vision-based maritime surveillance system using fused visual attention mapsMultimedia Tools and Applications10.1007/s11042-015-2512-x75:22(15051-15078)Online publication date: 1-Nov-2016
  • (2015)Temporally stable feature clusters for maritime object tracking in visible and thermal imagery2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS.2015.7301769(1-6)Online publication date: Aug-2015

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