Authors:
Ikhlef Bechar
;
Frederic Bouchara
;
Thibault Lelore
;
Vincente Guis
and
Michel Grimaldi
Affiliation:
Toulon University, France
Keyword(s):
Airborne Video System, Maritime Surveillance, Vessel Recognition, Dynamic Background, Chromatic Uncertainty, Dynamic Texture Uncertainty, MAP Estimation, Energy Minimization, Spatiotemporal Active Contours.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Segmentation and Grouping
;
Video Stabilization
;
Video Surveillance and Event Detection
Abstract:
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.