Abstract
This paper proposes a hybrid foreground object detection method suitable for the marine surveillance applications. Our approach combines an existing foreground object detection method with an image color segmentation technique to improve accuracy. The foreground segmentation method employs a Bayesian decision framework, while the color segmentation part is graph-based and relies on the local variation of edges. We also establish the set of requirements any practical marine surveillance algorithm should fulfill, and show that our method conforms to these requirements. Experiments show good results in the domain of marine surveillance sequences.
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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. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_43
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DOI: https://doi.org/10.1007/11558484_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
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