Abstract
In this paper, we present a robust and accurate water region detection technique developed for supporting ship identification. Due to the varying appearance of water body and frequent intrusion of ships, a region-based recognition is proposed. We segment the image into perceptually meaningful segments and find all water segments using a sampling-based Support Vector Machine (SVM). The algorithm is tested on 6 different port surveillance sequences and achieves a pixel classification recall of 97.5% and precision of 96.4%. We also apply our water region detection to support the task of multiple ship detection. Combined with our cabin detector, it successfully removes 74.6% false detections generated in the cabin detection process. A slight decrease of 5% in the recall value is compensated by a significant improvement of 15% in precision.
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© 2012 Springer-Verlag Berlin Heidelberg
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Bao, X., Zinger, S., Wijnhoven, R., de With, P.H.N. (2012). Water Region Detection Supporting Ship Identification in Port Surveillance. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_39
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DOI: https://doi.org/10.1007/978-3-642-33140-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33139-8
Online ISBN: 978-3-642-33140-4
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