Proceedings Article | 15 April 2008
Proc. SPIE. 6941, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XIX
KEYWORDS: Target detection, Image visualization, Infrared imaging, Detection and tracking algorithms, Visualization, Cameras, Sensors, Image sensors, Optical flow, Camouflage
In order to facilitate systematic, computer aided improvements of camouflage and concealment assessment methods,
the software system CART (Camouflage Assessment in Real-Time) was built up for the camouflage assessment
of objects in image sequences. Since camouflage success is directly correlated with the detection range of
target objects, the system supports the evaluation of image sequences with moving cameras. The main features
of CART comprise a semi-automatic annotation functionality for marking target objects (ground truth generation)
including a propagation of those markings over the image sequence, as well as a real-time evaluation of
the marked image regions by applying individually selected feature extractors. The system works with visualoptical,
infrared and SAR image data, which can be used separately or simultaneously. The software is designed
as a generic integration platform, which can be extended to further sensors, measurements, feature extractors,
methods, and tasks.
Besides the demand of using moving cameras, it is important to support also moving objects in the scene
(CACAMO - Computer Aided Camouflage Assessment of Moving Objects). Since moving objects are more
likely to be discovered than other ones, the state of movement obviously is a significant factor when designing
camouflage methods and should explicitly be incorporated into the assessment process. For this, the software
provides auto-annotation tools, as well as a specific movement measurement component in order to capture the
conspicuity depending on different moving states. The auto-annotation assistance for moving objects is done
with the aid of tracking algorithms, incorporating color information, optical flow and change detection using
Kalman and particle filters. The challenge is to handle semi or full camouflaged objects, a circumstance which
naturally hinders computer vision algorithms.
