Abstract
In the context of maritime boat ramps surveillance, this paper proposes an Adaptive Background Modeling method for Land and Water composition scenes (ABM-lw) to interpret the traffic of boats passing across boat ramps. We compute an adaptive learning rate to account for changes on land and water composition scenes, in which the portion of water changes over time due to tidal dynamics and other environmental influences. Experimental comparative tests and quantitative performance evaluations of real-world boat-flow monitoring traffic sequences demonstrate the benefits of the proposed algorithm.
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CITATION STYLE
Zhao, J., Pang, S., Hartill, B., & Sarrafzadeh, A. H. (2015). Adaptive background modeling for land and water composition scenes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9280, pp. 97–107). Springer Verlag. https://doi.org/10.1007/978-3-319-23234-8_10
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