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Proceedings Paper

Recognition of ships for long-term tracking
Author(s): Sebastiaan P. van den Broek; Henri Bouma; Henny E. T. Veerman; Koen W. Benoist; Richard J. M. den Hollander; Piet B. W. Schwering
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Paper Abstract

Long-term tracking is important for maritime situational awareness to identify currently observed ships as earlier encounters. In cases of, for example, piracy and smuggling, past location and behavior analysis are useful to determine whether a ship is of interest. Furthermore, it is beneficial to make this assessment with sensors (such as cameras) at a distance, to avoid costs of bringing an own asset closer to the ship for verification. The emphasis of the research presented in this paper, is on the use of several feature extraction and matching methods for recognizing ships from electro-optical imagery within different categories of vessels. We compared central moments, SIFT with localization and SIFT with Fisher Vectors. From the evaluation on imagery of ships, an indication of discriminative power is obtained between and within different categories of ships. This is used to assess the usefulness in persistent tracking, from short intervals (track improvement) to larger intervals (re-identifying ships). The result of this assessment on real data is used in a simulation environment to determine how track continuity is improved. The simulations showed that even limited recognition will improve tracking, connecting both tracks at short intervals as well as over several days.

Paper Details

Date Published: 20 June 2014
PDF: 12 pages
Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 909107 (20 June 2014); doi: 10.1117/12.2053295
Show Author Affiliations
Sebastiaan P. van den Broek, TNO (Netherlands)
Henri Bouma, TNO (Netherlands)
Henny E. T. Veerman, TNO (Netherlands)
Koen W. Benoist, TNO (Netherlands)
Richard J. M. den Hollander, TNO (Netherlands)
Piet B. W. Schwering, TNO (Netherlands)


Published in SPIE Proceedings Vol. 9091:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII
Ivan Kadar, Editor(s)

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