A hybrid color-based foreground object detection method for automated marine surveillance

Authors:
Daniel Socek;Dubravko Culibrk;Oge Marques;Hari Kalva;Borko Furht
Affiliations:
Center for Coastline Security Technologies (CCST), Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL;Center for Coastline Security Technologies (CCST), Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL;Center for Coastline Security Technologies (CCST), Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL;Center for Coastline Security Technologies (CCST), Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL;Center for Coastline Security Technologies (CCST), Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL
Venue:
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Year:
2005

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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.