Aggregated Dynamic Background Modeling

Standard practices in background modeling learn a separate model for every pixel in the image. However, in dynamic scenes the connection between an observation and the place where it was observed is much less important and is usually random. For example, a wave observed in an ocean scene could easily have been observed at another place in the image. Moreover, during a limited learning period, we cannot expect to observe at every pixel all the possible background behaviors. We therefore develop in this paper a background model in which observations are decoupled from the place in the image where they were observed. A single non-parametric model is used to describe the dynamic region of the scene, aggregating the observations from the whole region. Using high-order features, we demonstrate the feasibility of our approach on challenging ocean scenes using only grayscale information.

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Amit Adam, Ehud Rivlin, Ilan Shimshoni
• Keywords: Background Model, Challenging Ocean Scenes, ICIP 2006, Image Processing, Limited Learning Period, Possible Background Behaviors, Single Non-parametric Model