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This article addresses the problem of near real time video analysis of a maritime scene using a (moving) airborne RGB video camera in the goal of detecting and eventually recognizing a target maritime vessel. This is a very challenging problem mainly due to the high level of uncertainty of a maritime scene including a dynamic and noisy background, camera's and target's motions, and broad variability of background's versus target's appearances. We propose an approach which attempts to combine several types of spatiotemporal uncertainty in a single probabilistic framework. This allows to achieve a likelihood ratio with respect to any possible spatiotemporal configuration of the 2D+T video volume. Using the MAP estimation criterion, such a problem can be recast as as an energy minimization problem that we solve efficiently using a spatiotemporal active contour approach. We demonstrate the feasibility of the proposed approach using real maritime videos.

Uncertainty Fusion based Object Recognition and Tracking in Maritime Scenes using Spatiotemporal Active Contours