CFP last date
20 June 2024
Reseach Article

Identification and Classification of Objects in Marine Image Data Set for Coastal Surveillance

Published on December 2015 by Siddalingeshwar C. Jodalli, Vivek Simon, Vijayalakshmi M.n., andhe Dharani
National Conference on Power Systems and Industrial Automation
Foundation of Computer Science USA
NCPSIA2015 - Number 4
December 2015
Authors: Siddalingeshwar C. Jodalli, Vivek Simon, Vijayalakshmi M.n., andhe Dharani
18bf93a0-c4db-4119-a5c8-8cd0eb7138f3

Siddalingeshwar C. Jodalli, Vivek Simon, Vijayalakshmi M.n., andhe Dharani . Identification and Classification of Objects in Marine Image Data Set for Coastal Surveillance. National Conference on Power Systems and Industrial Automation. NCPSIA2015, 4 (December 2015), 24-26.

@article{
author = { Siddalingeshwar C. Jodalli, Vivek Simon, Vijayalakshmi M.n., andhe Dharani },
title = { Identification and Classification of Objects in Marine Image Data Set for Coastal Surveillance },
journal = { National Conference on Power Systems and Industrial Automation },
issue_date = { December 2015 },
volume = { NCPSIA2015 },
number = { 4 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 24-26 },
numpages = 3,
url = { /proceedings/ncpsia2015/number4/23351-7280/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Power Systems and Industrial Automation
%A Siddalingeshwar C. Jodalli
%A Vivek Simon
%A Vijayalakshmi M.n.
%A andhe Dharani
%T Identification and Classification of Objects in Marine Image Data Set for Coastal Surveillance
%J National Conference on Power Systems and Industrial Automation
%@ 0975-8887
%V NCPSIA2015
%N 4
%P 24-26
%D 2015
%I International Journal of Computer Applications
Abstract

Detection of objects of interest and finding out anomalies in the ports of sea is of a very high magnitude considering the low amount of current video analysis in maritime surveillance systems present. Nearly about 80% of all world trade is carried by sea transport. With the growing use of maritime transport, an increase of illegal activities from traffic of prohibited substances, to terrorist attacks using sea transport is constantly occurring. This work is motivated by the importance of this above said issue and that there are no major surveys on video detection for object recognition and analysis system in a marine environment for surveillance. In this paper we propose a novel method to recognize an object and detect it by its feature set and later can be utilized to differentiate anomalies encountered.

References
  1. D. Bloisi and L. Iocchi, Argos: A Video Surveillance System for Boat Traffic Monitoring in Venice, International Journal of Pattern Recognition and Artificial Intelligence, 2008, pp. 1-24.
  2. S. Fefilatyev, D. Goldgof, M. Shceve and C. Lembke, Detection and Tracking of Ships in Open Sea with Rapidly Moving Buoy-Mounted Camera System, Ocean Engineering, Vol. 54, 2012, pp. 1-12.
  3. S. Kasemi, S. Abghari, N. Lavesson, H. Johnson and P. Ryman, Expert Systems with Applications, Expert Systems with Applications, Vol. 40, 2013, pp. 5719-5729.
  4. D. Frost and J. -R. Tapamo, Detection and Tracking of Moving Objects in a Maritime Environment with Level-set with Shape Priors, EURASIP Journal on Image and Video Processing, Vol. 1, No. 42, 2013, pp. 1-16.
  5. K. M. Grupta, D. W. Aha, R. Hartley, and P. G. Moore, Adaptive Maritime Video Surveillance, Proceedings of SPIE, Vol. 7346, No. 09, 2009, pp. 1-12.
  6. H. Wei, H. Nyguien, P. Ramu, C. Raju, X. Liu and J. Yadegar, Automated Intelligence Video Surveillance System for Ships, Proceedings of SPIE Vol. 7306, No. 1N, 2009, pp. 1-12.
  7. Z. L. Szpak and J. R. Tapamo, Maritime Surveillance: Tracking Ships Inside a Dynamic Background Using a Fast Level-Set, Expert System with Applications, Vol. 38, 2011, pp. 6669-6680.
  8. D. Bloisi, L. Locchi, M. Fiorini and G. Grasiano, Automatic Maritime Surveillance with Visual Target Detection, CiteSeerX Scientific Literature Digital Library and Search Engine, 2011.
  9. H. Liu, O. Javed, G. Taylor, X. Cao and N. Haering, Omni-Directional Surveillance for Unmaned Water Vehicle, 8th International Workshop on Visual Surveillance, 2008.
  10. S. Fefilatyev, Detection of Marine Vehicles in Images and Videos of Open Sea, Master thesis (Computational Science and Engineering) – South Florida University, United States, Florida, Tampa, 2008.
  11. W. Kruger and Z. Orlov, Robust Layer-Based Boat Detection and Multi-Target-Tracking in Maritime Environments, Proceedings of Waterside Security Conference, 2010
  12. A. K. Bacho, F. Roux and F. Nicolls, An Optical Tracker for The Maritime Environment, Proceedings of SPIE, Signal Processing, Sensor Fusion, and Target Recognition XX, Vol. 8050, 2011.
  13. R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Transactions of the ASME – Journal of Basic Engineering, Vol. 82, 1960, pp. 35-45.
  14. F. Robert-Inácio, A. Raybaud and É. Clément, Multispectral Target Detection and Tracking for Seaport Video Surveillance, Proceedings of Image and Vision Computting, 2007, pp. 169-174
  15. R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Transactions of the ASME – Journal of Basic Engineering, Vol. 82, 1960, pp. 35-45.
  16. H. Schweitzer, R. Deng and R. F. Anderson, a Dual-Bound Algorithm for Very Fast and Exact Template Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 3, 2011.
  17. W. Kruger and Z. Orlov, Robust Layer-Based Boat Detection and Multi-Target-Tracking in Maritime Environments, Proceedings of Waterside Security Conference, 2010.
  18. M. Teutsch and W. Kruger, Classification of Small Boats in Infrared Images for Maritime Surveillance, Proceedings of Waterside Security Conference, 2010.
  19. D. Bloisi, L. Locchi, M. Fiorini and G. Grasiano, Automatic Maritime Surveillance with Visual Target Detection, CiteSeerX Scientific Literature Digital Library and Search Engine, 2011.
  20. B. Fisher," The RANSAC (Random Sample Consensus) Algorithm",http://homepages. inf. ed. ac. uk/rbf/CVonline/www. dai. ed. ac. uk
  21. M. A. Fischler, R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM, Vol 24, pp 381-395, 1981.
  22. Szpak, Z. L. . "Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set", Expert Systems With Applications, 2010, doi: 10. 1016/j. eswa. 2010. 11. 068
Index Terms

Computer Science
Information Sciences

Keywords

Maritime Surveillance System Object Recognition